EP2652621A1

EP2652621A1 - Intelligent code differencing using code clone detection

Info

Publication number: EP2652621A1
Application number: EP10860883.7A
Authority: EP
Inventors: Weipeng LIU; Gong CHENG; Sadi Khan; Song GE; Dongmei Zhang; Yingnong Dang
Original assignee: Microsoft Corp
Current assignee: Microsoft Technology Licensing LLC
Priority date: 2010-12-15
Filing date: 2010-12-15
Publication date: 2013-10-23
Also published as: CN103262047A; CA2967251A1; JP2013546090A; CA2820758A1; CA2967251C; JP5775599B2; WO2012079230A1; CN103262047B; KR101732764B1; EP2652621A4; KR20140001951A

Abstract

The subject disclosure relates to systems and methods for intelligent code differencing employing code clone detection technology. A large, complex source code change (e.g., moving and renaming functions across source files) may involve edits in multiple source files. As such, developers and/or code reviewers may have a difficult time identifying the large and complex changes, and determining which changes are most significant, using existing code differencing tools. Using code clone detection technology, different types of changes, either across source files or inside a particular source file may be determined. The changes can be categorized as new, duplicated, and deleted code snippets or functions, and moved, renamed or modified functions. For changes categorized as duplicated or modified, further categorization by the level of importance of the change can be made. For example, the change can be trivial, minor or significant. Visualization of the changes further provides intuitive understanding of the changes.

Description

INTELLIGENT CODE DIFFERENCING USING CODE CLONE

DETECTION

TECHNICAL FIELD

[0001] The subject disclosure generally relates to code differencing, or

"diffing," systems that intelligently generate and output semantic information.

BACKGROUND

[0002] A common task of code review is to be able to ascertain and appreciate the changes between a previous version of source code and a current version of source code. Conventional code differencing, or doffing, systems merely identify changes in source code or between versions of source code. These systems typically provide basic information, such as where there are added and/or deleted lines in one source file. A code reviewer reviewing the results of a conventional diffing system is therefore without intuitive or other descriptive information upon which to focus on the nature of the changes to the files, e.g., whether semantic or lexical changes have occurred.

[0003] Software development typically employs multiple software developers concurrently and collaboratively developing or modifying source code. To facilitate such development, the same source code, or source code base, e.g., a group of files containing source code, is often modified by different developers. Additionally, in some cases, one developer may fix bugs in the source code while another developer may concurrently modify the same source code by moving lines of source code within or across different files. Moreover, different versions of the same source code may be generated because of concurrent processing in two or more different branches by different developers. The foregoing approaches each generate different versions of the same code. The versions of code may thus be quite similar and, as such, the task of understanding the changes between versions of source code can be difficult.

[0004] While the above-described diffing systems provide a limited amount of support to developers, as mentioned, they are not particularly informative. The above- described deficiencies of today's code diffing systems are merely intended to provide an overview of some of the problems of conventional systems, and are not intended to be exhaustive. Other problems with conventional systems and corresponding benefits of the various non-limiting embodiments described herein may become further apparent upon review of the following description.

SUMMARY

[0005] A simplified summary is provided herein to help enable a basic or general understanding of various aspects of exemplary, non-limiting embodiments that follow in the more detailed description and the accompanying drawings. This summary is not intended, however, as an extensive or exhaustive overview. Instead, the sole purpose of this summary is to present some concepts related to some exemplary non-limiting embodiments in a simplified form as a prelude to the more detailed description of the various embodiments that follow.

[0006] Various embodiments as provided herein are targeted for integrated development environments (IDEs) wherein code clone detection technology can be employed. Various embodiments are also targeted for systems and techniques employing code clone detection methods. The code clone detection technology can be employed as a preliminary step in generating semantic information that is output to a code reviewer. Other environments and contexts that can benefit from the differencing techniques described herein are contemplated too.

[0007] Various embodiments employ code clone detection technology to generate semantic information about changes between versions of code. The semantic information can be a characterization of the change between the versions. The characterization can be output to the code reviewer and thereby aids software development generally, and code review processes, in particular. In some

embodiments, information can be output to the developer to review changes made by the developer him/herself and therefore embodiments described herein can aid in self- review of changes previously-entered by the developer and/or review of changes entered by third-parties and merely reviewed by the developer.

[0008] In some embodiments, visualization information can be generated such as architectural diagram or color coded information to display information about the changes between versions. The visualization information is output to a code reviewer for visually aiding the understanding of the changes.

[0009] Still further, other systems and methods described herein include a clone detection core and an importance engine in some embodiments. The clone detection component, or core, is configured to generate information indicative of a determination of a type of content change between at least two versions of source code. The importance engine is configured to determine a level of importance associated with the type of the content change, and output information indicative of the level of importance. The level of importance is related to the characterization of the change in some embodiments. In other embodiments, the level of importance is related to the type of change, e.g., whether a format, lexical or logical change. The level of importance can be expressed as a value, e.g., integer, fraction, percentage, etc., or as a visualization, e.g., color coding.

[0010] These and other embodiments are described in more detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Various non-limiting embodiments are further described with reference to the accompanying drawings in which:

[0012] Figure 1 is a block diagram showing an exemplary non-limiting implementation of an intelligent code diffing system in accordance with one or more embodiments;

[0013] Figure 2 is another block diagram showing an exemplary non-limiting implementation of an intelligent code diffing system in accordance with one or more embodiments;

[0014] Figure 3 is another block diagram showing an exemplary non-limiting implementation of an intelligent code diffing system in accordance with one or more embodiments;

[0015] Figure 4 is a flow diagram illustrating an exemplary non-limiting process for intelligent code diffing using code clone detection technology;

[0016] Figure 5 is another flow diagram illustrating an exemplary non- limiting process for intelligent code diffing using code clone detection technology;

[0017] Figure 6 is another flow diagram illustrating an exemplary non- limiting process for intelligent code diffing using code clone detection technology;

[0018] Figure 7 is another flow diagram illustrating an exemplary non- limiting process for intelligent code diffing using code clone detection technology;

[0019] Figure 8 is a block diagram illustrating an exemplary non-limiting screenshot for intelligent code diffing using code clone detection technology;

[0020] Figure 9 is another block diagram illustrating an exemplary non- limiting screenshot for intelligent code diffing using code clone detection technology; [0021] Figure 10 is another block diagram illustrating an exemplary non- limiting screenshot for intelligent code diffing using code clone detection technology;

[0022] Figure 11 is another block diagram illustrating an exemplary non- limiting screenshot for intelligent code diffing using code clone detection technology;

[0023] Figure 12 is another block diagram illustrating an exemplary non- limiting screenshot for intelligent code diffing using code clone detection technology;

[0024] Figure 13 is a block diagram showing an exemplary non-limiting implementation of a system architecture for implementing intelligent code diffing using code clone detection technology;

[0025] Figure 14 is a block diagram representing exemplary non-limiting networked environments in which various embodiments described herein can be implemented; and

[0026] Figure 15 is a block diagram representing an exemplary non-limiting computing system or operating environment in which one or more aspects of various embodiments described herein can be implemented.

DETAILED DESCRIPTION

OVERVIEW

[0027] By way of introduction, for efficiency in programming, software developers often duplicate sections of source code in numerous locations within programming projects. Reusing a portion of source code with or without some degree of modifications or adaptations is called "code cloning" and the resulting portions of code that match, or correspond, to one another with varying degrees of exactness, are called "code clones" or more simply, "clones." Additionally, in some embodiments, a group of one or more files of source code used to build a particular functionality, component or application is reused with or without some degree of modification within or between the files. The group of one or more files is called a "code base." While the term "code" is used herein for consistency, it should be understood that the term can apply to a "code base" where applicable.

[0028] Code clone detection technology addresses the problem of identifying and analyzing code clones in source code, or across files of source code. Typical code clone detection systems receive source code, pre-process the text of the source code to break lines into tokens and remove non-essential differences, and analyze the remaining code for similarities. [0029] Code diffing systems typically generate information about differences between two source code files. For example, differences between two source code files can be output. However, these systems typically output only basic information such as information describing lines that have been added or deleted.

[0030] Various embodiments as provided herein are targeted for integrated development environments (IDEs) wherein code clone detection technology can be employed. Various embodiments are also targeted for systems and techniques employing code clone detection methods. The code clone detection technology can be employed as a preliminary step in generating semantic information that is output to a code reviewer.

[0031] In one embodiment, a method of performing intelligent source code processing employing code clone detection technology comprises receiving at least two versions of source code; and processing the versions of the source code using code clone detection technology. Based on the code clone detection technology, a determination can be made as to different portions of the versions that correspond to one another.

[0032] Systems and methods described herein employ code clone detection technology to generate semantic information about changes between versions of code. The semantic information is a characterization of the change between the versions. The characterization is output to the code reviewer and thereby significantly aids software development generally, and the code review process, in particular.

[0033] In one embodiment, a method of performing intelligent source code processing employing code clone detection technology comprises receiving information indicative of a correspondence between at least two versions of source code. A characterization of the correspondence between the versions is determined. The characterization is indicative of a semantic difference between the versions, and is based on the correspondence. The information indicative of the characterization is output. In some embodiments, the information is output to a software development station that may be accessed by a code reviewer.

[0034] In some embodiments, the systems and methods also generate visualization information such as architectural diagram or color coded information to display information about the changes between versions. The visualization information is output to a code reviewer for visually aiding the understanding of the changes. [0035] Still further, other systems and methods described herein generate information indicative of a level of importance to one or more changes between versions of the code. The level of importance is related to the characterization of the change in some embodiments. In other embodiments, the level of importance is related to the type of change (e.g., whether a format, lexical or logical change, for example). The level of importance can be expressed as a value (e.g., integer, fraction, percentage) or as a visualization (e.g., color coding).

[0036] In one embodiment, a method of performing intelligent source code processing employing code clone detection technology comprises generating information indicative of a determination of a type of content change between at least two versions of source code. A level of importance associated with the type of the content change is determined. Information indicative of the level of importance is output. In some embodiments, a visualization of the level of importance of the change is output. The output can be provided to a software development station accessible by a code reviewer.

[0037] Herein, an overview of some of the embodiments for achieving intelligent code diffing has been presented above. As a roadmap for what follows next, various exemplary, non-limiting embodiments and features for intelligent code diffing are described in more detail. Then, some non-limiting implementations and examples are given for additional illustration, followed by representative network and

computing environments in which such embodiments and/or features can be implemented.

INTELLIGENT CODE DIFFING USING CODE CLONE DETECTION

[0038] It can be appreciated, however, that the embodiments provided herein are not intended to be limited to any specific database or system implementation.

Further, unless stated otherwise, the various embodiments are not intended to be limited to any specific code diffing or code cloning implementation(s).

[0039] By way of further description with respect to one or more non-limiting aspects of an intelligent code diffing design that can be employed to generate semantic information about changes between different versions of source code, various non-limiting characteristics associated with exemplary schemes that can be implemented are now described. For example, Fig. 1 is a block diagram showing an exemplary non-limiting implementation of an intelligent code differencing system 102. As shown in Fig. 1, code differencing system 102 is associated with one or more code base storage repositories 104, 106. In an embodiment, code base storage repositories 104, 106 store one or more code bases.

[0040] In one embodiment, code base storage repository 104 stores a first version of source code while code base storage repository 106 stores a second version of source code. As such, a first version of source code 110 and a second version of source code 120 can be received by the code differencing system 102 from code base storage repositories 104, 106. However, the code bases need not be so stored and, for example, both versions of code bases can be stored in and received from the same code base storage repository 104 or 106.

[0041] Additionally, while the number of versions of source code is indicated as two in some embodiments, different numbers of versions of source code can be processed simultaneously or concurrently in some non-limiting embodiments. For example, three or more versions of source code can be processed concurrently or sequentially using the systems and methods described herein.

[0042] Further, versions of source code can be generated as a result of different types of processes. For example, the two versions of source code can be the same source code that has been processed in two different branches by different developers. As another example, in another embodiment, the two versions of source code can be a first version of source code created at time, t, and a second version of source code created by modifying the first version of source code at a time, t+x. For example, a first version of source code could be created and a second version of source code could be later created based on modifying the first version to address bugs in source code or to provide other enhancements.

[0043] Code differencing system 102 includes a pre-processor 130 configured to pre-process the first and second versions of the source code 110, 120 to prepare the source code for code clone detection. For example, the first and second versions of source code 110, 120 can be received by the code differencing system 102 and the pre-processor 130 can break lines into tokens and remove non-essential differences between the versions of source code 110, 120.

[0044] Code differencing system 102 includes a code clone detection core 140.

The code clone detection core 140 can perform the functions of code clone detection and processing. [0045] In one non-limiting embodiment, the code clone detection that is performed is as described in U. S. patent application number 12/752,942, filed April 1 , 2010, and entitled "CODE-CLONE DETECTION AND ANALYSIS," which is herein incorporated by reference in its entirety, though for the avoidance of doubt, the various embodiments described herein are not limited to any particular code clone detection technology. The only requirement to a particular code clone detection technology is being able to detect both exact code clones and near-miss code clones. Near-miss code clones are those wherein further modifications such as add, delete and/or edit are performed on the source code after duplication.

[0046] In another embodiment, code clone detection includes identifying one or more portions within the two versions of source code that are similar to one another. As such, the code clone detection can detect portions that are not the same as one another and that are merely similar to one another. By way of further clarification, the code clone detection core 140 is configured to identify code that has varying degrees of similarity. As such, the code clone detection core 140 is able to detect portions, e.g., snippets, of code within the versions of code that are different but similar.

[0047] In some embodiments, for example, portions that are similar to one another are portions that are modified relative to one another with a deleted code snippet or function, a new code snippet or function, a duplicated code snippet or function, a moved function, a renamed function, a combination of moved and renamed function, a modified function or the like.

[0048] In one embodiment, identifying the similar pieces of code includes outputting two code snippets, one code snippet from each of the versions of code. However, the embodiments herein are not so limited and identifying the similar pieces of code can include outputting information indicative of a location or description of the two code snippets within the two pieces of code. As shown in Fig. 1 , similar pieces of code (or location or other information identifying the similar pieces of code) are output from the code differencing system 102. In one embodiment, the similar pieces of code or location or other information identifying the similar pieces of code is output to a software development station 150.

[0049] In one embodiment, the software development station 150 is located proximate to the code differencing system 102. In other embodiments, the software development station 150 is located remote from the code differencing system 102. For example, in some cases, the code differencing system 102 is associated with a server and the software development station 150 is associated with a client machine that accesses the code differencing system 102 over a network.

[0050] The detected code snippets can be analyzed by a code reviewer accessing the software development station 150 to enable the code reviewer to focus on the portions of the versions of code that has been changed. In large code bases or large files of code, the ability to focus on a selected portion can significantly reduce the outlay of time and resources.

[0051] In some embodiments, the code clone detection core 140 is also configured, to generate a value corresponding to the degree of similarity between the two versions of source code 110, 120. The value may be an integer, fraction or percentage value.

[0052] Fig. 2 is another block diagram showing an exemplary non-limiting implementation of an intelligent code differencing system 202. As described with reference to Fig. 1, code differencing system 202 includes a pre-processor 130 and code clone detection core 140. The code differencing system 202 receives first and second versions of source code 110, 120. In one non-limiting embodiment, as shown in Fig. 2, the first and second versions of source code 110, 120 are received from the code base storage repositories 104, 106.

[0053] As shown in Fig. 2, the code differencing system 202 also includes a characterization engine 210 determine a correspondence between the two versions of source code 110, 120. The correspondence is indicative of the change between the two versions of source code 110, 120, and is associated with a characterization. The characterization includes semantic information about the changes between the two versions of source code 110, 120.

[0054] As such, the code clone detection core 140 identifies similar portions of the source code and output such information to the characterization engine 210. The characterization engine 210 abstracts that information to extract semantic meaning describing the change between the two codes. The semantic meaning is described by the characterization, which is output from the characterization engine 210.

[0055] There are a number of different types of characterizations possible in various embodiments. For example, in one case, there are approximately six different characterizations of changes between the first and second versions of source code 110, 120. In one embodiment, the different characterizations are new code snippets or functions (e.g., a new code snippet or function is added in one version of code relative to the other version of code is the change); duplicated code snippet or function (e.g., a new code snippet or function is duplicated by copy-and-paste); a deleted code snippet or function (e.g., a function is deleted in one version of code relative to the other version of code); a moved function (e.g., a function is moved from the source file for one version of code to the source file for the other version of code); a renamed function (e.g., a signature is changed in the code but the content has not changed); or a modified function (e.g., a signature is the same but the content has changed in the code). In various embodiments, a characterization can also be indicative of a code snippet or function having moved and renamed code snippets or functions combined. As used herein, the term "code snippet" means a segment of consecutive statements in a function.

[0056] The above six characterizations are output from the code differencing system 202 to the software development station 150. The characterization engine 210 can therefore provide semantic information to the software development station 150 (and code reviewer accessing such software development station 150) about what type of change occurred between the first and second versions of source code 110, 120 as opposed to merely providing the code reviewer with the changes and requiring the code reviewer to assess the type of changes that occurred. The code review experience is therefore improved and made more efficient.

[0057] In one embodiment, as shown in Fig. 2, the code differencing system

202 also includes a visualization engine 220. The visualization engine 220 generates a visualization of the characterization. The visualization is a file describing the characterization, an architectural diagram graphically displaying the changes between the versions of source code 110, 120 or the characterization, and/or a color-coded listing of functions associated with the changes between the first and second versions of source code 110, 120 identified by the code clone detection core 140.

[0058] Information indicative of the characterization and/or the visualization of the characterization is output to the software development station 150 from the characterization engine 210 and/or the visualization engine 220, respectively.

[0059] Fig. 3 is another block diagram showing exemplary non-limiting implementation of an intelligent code differencing system 302. As described with reference to Fig. 1, code differencing system 302 includes a pre-processor 130 and code clone detection core 140. The code differencing system 302 receives first and second versions of source code 110, 120. In one non-limiting embodiment, as shown in Fig. 3, the first and second versions of source code 110, 120 are received from the code base storage repositories 104, 106.

[0060] As shown in Fig. 2, code differencing system 302 includes a characterization engine 210 and, in some embodiments, a visualization engine 220. As shown in Fig. 3, code differencing system 302 also includes an importance engine 310 configured to associate a level of importance with the type of content change between the two versions of source code 110, 120 and/or based on a characterization generated by the characterization engine 210.

[0061] In some non-limiting embodiments wherein the type of content change is a characterization of the change, the level of importance is mapped from the associated characterization. For example, a moved function can be a characterization that is then mapped to a trivial level of importance change, thereby being assigned the lowest level of importance. As another example, a renamed function can be mapped to a minor change, thereby being assigned a moderate level of importance. As another example, the modified function can be mapped to a major change, thereby being assigned the greatest level of importance. Referring to the previously-described characterizations, level of importance information can be provided for duplicated code snippet or function, moved function, renamed function or modified function characterizations.

[0062] In some embodiments, in lieu of, or in addition to, generating level of information based on the characterization, the level of importance can be generated based on the type of content change from the first to the second versions of the source code 110, 120.

[0063] For example, in one embodiment, the type of content change is a format change or a comment change. Format and comment changes can be associated with a trivial level of change and therefore assigned the lowest level of importance.

[0064] In another embodiment, the type of content change is a lexical change

(e.g., variable re-naming). Lexical changes can be associated with a moderate level of change and therefore assigned a moderate level of importance.

[0065] In another embodiment, the type of content change is a logical change.

A logical change can be associated with a major level of change, and therefore assigned a highest level of importance. [0066] In some non-limiting embodiments, the level of importance is determined based on assigning a numeric (e.g., percentage or otherwise) value to the type of the content change and categorizing the level of importance according to the numeric value. For example, a value of a 5% change would represent a trivial change, which would be assigned the lowest level of importance, while a value of a 50% (or more) change would be a major change, which would be assigned the greatest level of importance. Additionally, in various embodiments, the value (e.g., the 5% value above) is utilized in conjunction with other code metrics to further enhance to ability to communicate the importance of a change. In one non-limiting embodiment, for example, if 5% of code has changed semantically but the portion that has changed is the critical path of the application (based on test results), the relative importance of the actual portion of the code that has changed can be communicated by placing a value on a code metric associated with the portion of the code and/or by selecting a particular metric, value of a metric, importance level of a metric, etc. to communicate the importance of the portion of the code that has changed.

[0067] The level of importance is output from the importance engine 310 to the software development station 150. In one embodiment, although not shown in Fig. 3, the visualization engine 220 receives information about the level of importance generated by the importance engine 310, and generates and outputs the level of importance as a visual representation.

[0068] Fig. 4 is a flow diagram illustrating an exemplary non-limiting process for performing intelligent code diffing using code clone detection technology. At 400, at least two versions of source code are received at the intelligent code diffing system. At 410, the two versions of source code are processed using code clone detection technology, and similar pieces of source code are identified. By way of further clarification, the code clone detection technology is configured to identify source code that has varying degrees of similarity. As such, the code clone detection technology is able to detect snippets of source code that are different but that are similar.

[0069] In one embodiment, identifying the similar pieces of source code includes outputting two source code snippets, one source code snippet from each of the versions of source code. However, the embodiments herein are not so limited and identifying the similar pieces of source code can include outputting information indicative of a location or description of the two source code snippets within the two pieces of source code. [0070] At 420, the source code snippets (or information indicative of the location or description of the source code snippets) are processed by the code diffing system. Semantic analysis on the similar pieces of source code is performed to determine a characterization of the differences between the two pieces of source code.

[0071] Step 420 is described in greater detail in one non-limiting embodiment as follows. In one implementation, the code clone detection tool outputs a clone pair set {Pi},i =1, 2, ... , N, where P; = [SAi, SB;], SA is one code snippet (or function) from one version of source files, and SB; is one code snippet (or function) of another version of source files. At 420, then, each clone pair Pi is further analyzed.

[0072] Specifically, in one case, if S A and SB; are exactly same and with the same location context (e.g., in the same source file and having the same neighboring functions), then this pair is ignored.

[0073] In another case, if S A and SB; are exactly the same functions but having different location context (e.g., in different source files or having different neighboring functions), then SAi and SB; are characterized as moved functions.

[0074] In another case, if S A and SB; are near-miss cloned functions, they will be further categorized as follows: if their signatures are different and have the same body content, then they are characterized as renamed functions. If their body content are different, but have the same signature, then they are characterized as modified functions. Further, if they have different location context, then they are characterized as moved functions. If they have further different body content, they are characterized as moved and modified functions.

[0075] In addition, also at 420, code snippets and functions that are not in the list of set {Pi} are further analyzed to get deleted and/or added code snippets or functions. The deleted and/or added code snippets or functions are further searched against 13213 (e.g., with an index of a local code base) or 1334 (e.g., with an index of a set of code bases in the server side) to determine if they are duplicated from other places in the current code base or even from other code bases.

[0076] Turning back to FIG. 4, as described above, the characterization is based on the level and/or type of complexity of the changes between the two versions of source code in some embodiments.

[0077] As such, the process of Fig. 4, provides information about what type of change occurred as opposed to merely providing the code reviewer with the change and requiring the code reviewer to assess the type of change that occurred. The code review experience is therefore improved.

[0078] While not shown in Fig. 4, in some embodiments, the characterization is output as a file that describes the differences between the versions of source code. However, a file is one non-limiting example.

[0079] In other non-limiting embodiments, for example, the characterization

(or the change that the characterization indicates) is output visually. For example, the visualization can be that of an architectural diagram that graphically depicts the operations on the two versions of source code that result in the characterization. For example, a move operation could be depicted visually by indicating the function of interest and illustrating an arrow from the function in the first version of source code to the function in the second version of source code. As another example, the visualization can be a color-coded diagram that illustrates different color-coded code snippets or functions or pieces of source code from the two versions of source code. The colors associated with the code snippets or functions or pieces of source code are assigned to different characterizations (e.g., red can represent a moved function, while yellow represents a duplicated code snippet or function, for example). The code reviewer can visually identify the type of changes between the two versions of source code.

[0080] While six different characterizations are described, these

characterizations are non-limiting and merely exemplary. Other characterizations are possible and, in some cases, a smaller number (or greater number) of characterizations is possible as determined by the system designer and the needs of the code reviewer, which may change from time to time.

[0081] The process of Fig. 4 can be separated into different processes, each of which having novelty and distinctive advantages over the conventional approaches and uses of code clone detection technology in general, and code diffing, in particular.

[0082] For example, Fig. 5 is a flow diagram illustrating an exemplary non- limiting process for facilitating intelligent code diffing. At 500, two versions of source code are received. At 510, the two versions of source code are processing using code clone detection technology. At 520, using the code clone detection technology, different portions of the source code that correspond to one another are determined. In non-limiting embodiments, the pieces of source code that correspond to one another are either the same pieces of source code or similar pieces of source code. As such, the process of Fig. 5 utilizes code clone detection technology to detect near matches of source code (and not merely exact matches of source code).

[0083] The detected source code can be output from an intelligent code diffing system and accessed by a code reviewer.

[0084] As another example, Fig. 6 is a flow diagram illustrating an exemplary non-limiting process for facilitating intelligent code diffing. At 600, information indicative of a correspondence between two versions of source code is received. At 610, a characterization of the correspondence is determined. In one non-limiting embodiment, the correspondence is information indicative of the actual differences between the two versions of source code that is abstracted to determine

characterizations. For example, in one non-limiting embodiment, the correspondence is information indicative of a code snippet or function being deleted from a first version and a code snippet or function being added in the second version. By contrast, the characterization is the abstraction to the semantic meaning underlying the added and deleted code snippet or function. For example, if the added and deleted code snippet or function are the same code snippet or function, the semantic meaning of the deletion and addition is determined to be a move from one version to another version and the characterization is then determined to be a move described above with reference to Fig. 4 (as opposed to merely outputting that a deletion of a first file and an addition of a second file occurred across the two versions with no addition information as to whether the added and deleted content was the same or any other semantic information associated therewith).

[0085] At 620, a visualization of the characterization is generated. As described previously, the visualization is a file describing the characterization of the changes between versions, an architectural diagram graphically displaying the change between the version and/or a color-coded listing of code snippet or functions that represent a change in one version of source code relative to the other version of source code.

[0086] At 630, the characterization is output. In some embodiments, the changes that drive the identification of the characterization are also output.

[0087] As another example, Fig. 7 is a flow diagram illustrating an exemplary non-limiting process for facilitating intelligent code diffing. At 700, information indicative of a type of content change between versions of source code is generated. The type of content change can be a format change or comment change; a lexical change (e.g., variable re-naming) or a logical change in different non-limiting embodiments. Further, in some non-limiting embodiments, the type of content change is the characterization of the differences between the versions of source code. As such, the type of content change is a moved function, deleted code snippet or function or any number of the other types of code snippet and/or functions described above with reference to Figs. 4-6, for example.

[0088] At 710, a level of importance associated with the type of the content change is determined. In some non-limiting embodiments wherein the type of content change is a characterization of the change, the level of importance is mapped from the associated characterization. For example, a moved function can be a characterization mapped to a trivial change, thereby being assigned the lowest level of importance. As another example, a renamed function can be mapped to a minor change, thereby being assigned a moderate level of importance. As another example, the modified function can be mapped to a major change, thereby being assigned the greatest level of importance. Referring to the previously-described characterizations, level of importance information can be provided for duplicated code snippet or function, moved function, renamed function or modified function characterizations.

[0089] In some non-limiting embodiments, the level of importance can be determined based on assigning a numeric (e.g., percentage or otherwise) value to the type of the content change and categorizing the level of importance according to the numeric value. For example, a value of a 5% change would represent a trivial change, which would be assigned the lowest level of importance while a value of a 50% (or more) change would represent a major change, which would be assigned the greatest level of importance.

[0090] At 720, information indicative of the level of importance is output. As described with regard to Figs. 4 and 6, the level of importance can be depicted visually.

[0091] Turning to Figs. 8-12, block diagrams illustrating exemplary screenshot for intelligent code diffing are shown. Fig. 8 is a block diagram

illustrating an exemplary non-limiting screenshot for intelligent code diffing using code clone detection technology. As shown in Fig. 8, a developer made changes to multiple files of source code. The files are compared to previous versions and information indicative of the change is indicated in the screenshot for each edited file. Additionally, new files that are added relative to previous versions are indicated as added.

[0092] Fig. 9 is another block diagram illustrating an exemplary non-limiting screenshot for intelligent code diffing using code clone detection technology. The code diffing system having embodiments described herein outputs information indicative of function-level, intuitive changes to the versions of the source code, as shown in Fig. 9. The changes are labeled as one of the six characterizations previously-described herein. As described herein, in various embodiments, the amount of change can also be indicated by the code diffing system. By way of example, the percentage of change between the versions (or the percentage of similarity between the versions) is indicated along with the characterization in some embodiments.

[0093] Fig. 10 is another block diagram illustrating an exemplary non-limiting screenshot for intelligent code diffing using code clone detection technology. The code diffing system can receive inputs selecting a file such as the selection of NewFile.cs as shown in Fig. 10. The detailed changes between the versions is described at a function, intuitive level. For example, for a duplicated code snippet or function, such as function NewFile: :CopiedMethodl in file newFile.cs, the code diffing system shows the detailed changes after duplication, including signature (e.g., function definition) change and content (e.g., body) change (91% similarity).

[0094] By clicking an icon (or the file of interest) the detailed changes can also be output in an intuitive, semantic manner, as shown in Fig. 11. In this embodiment, the change is a bug fix.

[0095] Fig. 12 is another block diagram illustrating an exemplary non-limiting screenshot for intelligent code diffing using code clone detection technology. Fig. 12 shows the detailed changes between a function (NewFile: :CopiedMethodl in file newFile.cs shown in Fig. 10) and its duplicated version.

[0096] Fig. 13 is a block diagram showing an exemplary non-limiting implementation of a system architecture for implementing intelligent code diffing using code clone detection technology. As shown in Fig. 13, the system includes a code clone analysis core 1310, an integrated development environment (IDE) 1320 and an application server 1350.

[0097] The code clone analysis core 1310 includes an indexer 1312 and parser

1314 to respectively perform indexing and parsing associated with the code received by the code clone analysis core 1310. A parser interface 1315 is provided. The code clone analysis core 1310 includes a code clone detector 1316 configured to detect similar pieces of code. In one non-limiting embodiment, as mentioned, the code clone detector 1316 is configured to code clone detection and processing described with reference to U.S. patent application number 12/752,942, filed April 1, 2010, titled "CODE-CLONE DETECTION AND ANALYSIS." In addition to the code clone detection capabilities, the code clone detector 1316 can be configured to characterize the changes between different versions of code at a semantic level and/or generally perform any of the functions described herein with reference to the characterization engine 210 of Fig. 2 and/or the importance engine 310 of Fig. 3.

[0098] The code clone analysis core 1310 also includes a difference visualizer

1318 configured to generate information for visualization of a characterization and/or level of importance as previously-described with reference to the visualization engine 220 of Fig. 2.

[0099] The IDE 1320 includes an augmented code review user interface (UI)

1322, a code clone provider 1324 that includes an analysis driver 1326 and a code clone analysis core 1328. A language services component 1340 and functionalities for receiving information from other data providers are also provided at 1342.

[00100] Turning now to the application server 1350, as noted above, in some embodiments, the system also includes a code clone analysis service 1330 as part of the application server 1350. The code clone analysis service 1330 includes a code clone analysis core 1332 at a server for providing intelligent code diffing, a code clone search engine 1334 and a code clone detection engine 1336. The application server 1350 can also include existing services 1352 and a task manager 1354.

[00101] When the application server 1350 includes the code clone analysis service 1330, indexing of large scale code bases (e.g., tens to hundreds of millions of lines of code from multiple code bases) can be processed and users can search code clones in larger scope. This enables the possibility of determining a code snippet or function that is a duplication of the source code in other solutions, although it could be new in the current solution. This further facilitates the developers/code reviewers' ability to understand the context of the code changes better. Without the code clone analysis service 1330, the intelligent code diffing is performed at the client utilizing the code clone analysis core 1310 and the IDE 1320 and relatively small code bases can be processed, however, such solutions are particularly light weight and convenient.

EXEMPLARY NETWORKED AND DISTRIBUTED ENVIRONMENTS

[00102] One of ordinary skill in the art can appreciate that the various embodiments of the intelligent code diffing systems and methods described herein can be implemented in connection with any computer or other client or server device, which can be deployed as part of a computer network or in a distributed computing environment, and can be connected to any kind of data store. In this regard, the various embodiments described herein can be implemented in any computer system or environment having any number of memory or storage units, and any number of applications and processes occurring across any number of storage units. This includes, but is not limited to, an environment with server computers and client computers deployed in a network environment or a distributed computing

environment, having remote or local storage.

[00103] Distributed computing provides sharing of computer resources and services by communicative exchange among computing devices and systems. These resources and services include the exchange of information, cache storage and disk storage for objects, such as files. These resources and services also include the sharing of processing power across multiple processing units for load balancing, expansion of resources, specialization of processing, and the like. Distributed computing takes advantage of network connectivity, allowing clients to leverage their collective power to benefit the entire enterprise. In this regard, a variety of devices may have applications, objects or resources that may participate in the mechanisms as described for various embodiments of the subject disclosure.

[00104] Fig. 14 provides a schematic diagram of an exemplary networked or distributed computing environment. The distributed computing environment comprises computing objects 1410, 1412, etc. and computing objects or devices 1420, 1422, 1424, 1426, 1428, etc., which may include programs, methods, data stores, programmable logic, etc., as represented by applications 1430, 1432, 1434, 1436, 1438. It can be appreciated that computing objects 1410, 1412, etc. and computing objects or devices 1420, 1422, 1424, 1426, 1428, etc. may comprise different devices, such as personal digital assistants (PDAs), audio/video devices, mobile phones, MP3 players, personal computers, laptops, etc. [00105] Each computing object 1410, 1412, etc. and computing objects or devices 1420, 1422, 1424, 1426, 1428, etc. can communicate with one or more other computing objects 1410, 1412, etc. and computing objects or devices 1420, 1422, 1424, 1426, 1428, etc. by way of the communications network 1440, either directly or indirectly. Even though illustrated as a single element in Fig. 14, communications network 1440 may comprise other computing objects and computing devices that provide services to the system of Fig. 14, and/or may represent multiple

interconnected networks, which are not shown. Each computing object 1410, 1412, etc. or computing object or device 1420, 1422, 1424, 1426, 1428, etc. can also contain an application, such as applications 1430, 1432, 1434, 1436, 1438, that might make use of an API, or other object, software, firmware and/or hardware, suitable for communication with or implementation of the various embodiments of the subject disclosure.

[00106] There are a variety of systems, components, and network

configurations that support distributed computing environments. For example, computing systems can be connected together by wired or wireless systems, by local networks or widely distributed networks. Currently, many networks are coupled to the Internet, which provides an infrastructure for widely distributed computing and encompasses many different networks, though any network infrastructure can be used for exemplary communications made incident to the code differencing systems as described in various embodiments.

[00107] Thus, a host of network topologies and network infrastructures, such as client/server, peer-to-peer, or hybrid architectures, can be utilized. The "client" is a member of a class or group that uses the services of another class or group to which it is not related. A client can be a process, i.e., roughly a set of instructions or tasks, that requests a service provided by another program or process. The client process utilizes the requested service without having to "know" any working details about the other program or the service itself.

[00108] In a client/server architecture, particularly a networked system, a client is usually a computer that accesses shared network resources provided by another computer, e.g., a server. In the illustration of Fig. 14, as a non-limiting example, computing objects or devices 1420, 1422, 1424, 1426, 1428, etc. can be thought of as clients and computing objects 1410, 1412, etc. can be thought of as servers where computing objects 1410, 1412, etc., acting as servers provide data services, such as receiving data from client computing objects or devices 1420, 1422, 1424, 1426, 1428, etc., storing of data, processing of data, transmitting data to client computing objects or devices 1420, 1422, 1424, 1426, 1428, etc., although any computer can be considered a client, a server, or both, depending on the circumstances. Any of these computing devices may be processing data, or requesting transaction services or tasks that may implicate the differencing techniques as described herein for one or more embodiments.

[00109] A server is typically a remote computer system accessible over a remote or local network, such as the Internet or wireless network infrastructures. The client process may be active in a first computer system, and the server process may be active in a second computer system, communicating with one another over a communications medium, thus providing distributed functionality and allowing multiple clients to take advantage of the information-gathering capabilities of the server. Any software objects utilized pursuant to the techniques described herein can be provided standalone, or distributed across multiple computing devices or objects.

[00110] In a network environment in which the communications network 1440 or bus is the Internet, for example, the computing objects 1410, 1412, etc. can be Web servers with which other computing objects or devices 1420, 1422, 1424, 1426, 1428, etc. communicate via any of a number of known protocols, such as the hypertext transfer protocol (HTTP). Computing objects 1410, 1412, etc. acting as servers may also serve as clients, e.g., computing objects or devices 1420, 1422, 1424, 1426, 1428, etc., as may be characteristic of a distributed computing environment.

EXEMPLARY COMPUTING DEVICE

[00111] As mentioned, advantageously, the techniques described herein can be applied to any device where it is desirable to perform code differencing. It can be understood, therefore, that handheld, portable and other computing devices and computing objects of all kinds are contemplated for use in connection with the various embodiments. Accordingly, the below general purpose remote computer described below in Fig. 15 is but one example of a computing device.

[00112] Although not required, embodiments can partly be implemented via an operating system, for use by a developer of services for a device or object, and/or included within application software that operates to perform one or more functional aspects of the various embodiments described herein. Software may be described in the general context of computer-executable instructions, such as program modules, being executed by one or more computers, such as client workstations, servers or other devices. Those skilled in the art will appreciate that computer systems have a variety of configurations and protocols that can be used to communicate data, and thus, no particular configuration or protocol should be considered limiting.

[00113] Fig. 15 thus illustrates an example of a suitable computing system environment 1500 in which one or aspects of the embodiments described herein can be implemented, although as made clear above, the computing system environment 1500 is only one example of a suitable computing environment and is not intended to suggest any limitation as to scope of use or functionality. Neither should the computing system environment 1500 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary computing system environment 1500.

[00114] With reference to Fig. 15, an exemplary remote device for

implementing one or more embodiments includes a general purpose computing device in the form of a computer 1510. Components of computer 1510 may include, but are not limited to, a processing unit 1520, a system memory 1530, and a system bus 1522 that couples various system components including the system memory to the processing unit 1520.

[00115] Computer 1510 typically includes a variety of computer readable media and can be any available media that can be accessed by computer 1510. The system memory 1530 may include computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) and/or random access memory (RAM). Computer readable media can also include, but is not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic strip), optical disks (e.g., compact disk (CD), digital versatile disk (DVD)), smart cards, and/or flash memory devices (e.g., card, stick, key drive). By way of example, and not limitation, system memory 1530 may also include an operating system, application programs, other program modules, and program data.

[00116] A user can enter commands and information into the computer 1510 through input devices 1540. A monitor or other type of display device is also connected to the system bus 1522 via an interface, such as output interface 1550. In addition to a monitor, computers can also include other peripheral output devices such as speakers and a printer, which may be connected through output interface 1550. [00117] The computer 1510 may operate in a networked or distributed environment using logical connections to one or more other remote computers, such as remote computer 1570. The remote computer 1570 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, or any other remote media consumption or transmission device, and may include any or all of the elements described above relative to the computer 1510. The logical connections depicted in Fig. 15 include a network 1572, such local area network (LAN) or a wide area network (WAN), but may also include other networks/buses. Such networking environments are commonplace in homes, offices, enterprise-wide computer networks, intranets and the Internet.

[00118] As mentioned above, while exemplary embodiments have been described in connection with various computing devices and network architectures, the underlying concepts may be applied to any network system and any computing device or system.

[00119] Also, there are multiple ways to implement the same or similar functionality, e.g., an appropriate application programming interface (API), tool kit, driver source code, operating system, control, standalone or downloadable software object, etc. which enables applications and services to take advantage of techniques provided herein. Thus, embodiments herein are contemplated from the standpoint of an API (or other software object), as well as from a software or hardware object that implements one or more aspects of the diffing techniques described herein. Thus, various embodiments described herein can have aspects that are wholly in hardware, partly in hardware and partly in software, as well as in software.

[00120] The word "exemplary" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used, for the avoidance of doubt, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements. [00121] As mentioned, the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination of both. As used herein, the terms "component," "system" and the like are likewise intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on computer and the computer can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers.

[00122] The aforementioned systems have been described with respect to interaction between several components. It can be appreciated that such systems and components can include those components or specified sub-components, some of the specified components or sub-components, and/or additional components, and according to various permutations and combinations of the foregoing. Subcomponents can also be implemented as components communicatively coupled to other components rather than included within parent components (hierarchical).

Additionally, it is noted that one or more components may be combined into a single component providing aggregate functionality or divided into several separate subcomponents, and that any one or more middle layers, such as a management layer, may be provided to communicatively couple to such sub-components in order to provide integrated functionality. Any components described herein may also interact with one or more other components not specifically described herein but generally known by those of skill in the art.

[00123] In view of the exemplary systems described supra, methodologies that may be implemented in accordance with the described subject matter can also be appreciated with reference to the flowcharts of the various figures. While for purposes of simplicity of explanation, the methodologies are shown and described as a series of blocks, it is to be understood and appreciated that the various embodiments are not limited by the order of the blocks, as some blocks may occur in different orders and/or concurrently with other blocks from what is depicted and described herein. Where non-sequential, or branched, flow is illustrated via flowchart, it can be appreciated that various other branches, flow paths, and orders of the blocks, may be implemented which achieve the same or a similar result. Moreover, not all illustrated blocks may be required to implement the methodologies described hereinafter.

[00124] In addition to the various embodiments described herein, it is to be understood that other similar embodiments can be used or modifications and additions can be made to the described embodiment(s) for performing the same or equivalent function of the corresponding embodiment(s) without deviating therefrom. Still further, multiple processing chips or multiple devices can share the performance of one or more functions described herein, and similarly, storage can be effected across a plurality of devices. Accordingly, the invention should not be limited to any single embodiment, but rather should be construed in breadth, spirit and scope in accordance with the appended claims.

Claims

CLAIMS What is claimed is:

1. A method of performing intelligent code differencing employing code clone detection, comprising:

executing instructions by at least one processor that, in response to execution, cause operations to be performed, including:

receiving at least two versions of source code;

processing the at least two versions of the source code using code clone detection; and

determining different portions of the at least two versions of source code that correspond to one another based, at least, on an output of the code clone detection.

2. The method of claim 1, further comprising outputting at least two portions of code, wherein the at least two portions of code respectively correspond to the portions of the at least two versions of source code that correspond to one another.

3. The method of claim 1, wherein the at least two versions of source code comprises a first version of source code and a second version of the source code, and wherein the processing includes processing the first version in a first branch, and processing the second version in a second branch.

4. The method of claim 1, wherein the determining comprises determining the different portions of the at least two versions of the source code that have a level of similarity to one another that is greater than a predetermined level of similarity.

5. The method of claim 1, further comprising accessing the code clone detection provided in a server from a client device over a network.

6. A method of performing intelligent code differencing employing code clone detection, comprising: receiving, by at least one computing device, first information indicative of a correspondence between at least two versions of source code;

determining, based on code clone detection technology, a characterization of the correspondence between the at least two versions of source code, wherein the characterization includes semantic information; and

outputting second information indicative of the characterization.

7. The method of claim 6, wherein the determining the characterization includes determining at least one of a new code snippet or function, a duplicated code snippet or function, a moved function, a renamed function, a modified function, a deleted code snippet or function or a combination of moved and renamed function.

8. The method of claim 6, wherein the determining the characterization includes determining a moved function, and wherein the determining the characterization comprises determining that a first function was deleted from a first one of the at least two versions of source code, and determining that the function was added to a second one of the at least two versions of the source code.

9. The method of claim 6, further comprising generating a visualization of the characterization.

10. The method of claim 9, wherein the generating the visualization comprises generating a portion of the at least two versions of source code color-coded in one or more colors, wherein the one or more colors are associated with one or more characterizations.

11. The method of claim 9, wherein the generating the visualization comprises generating a diagram including one or more components representing one or more different characterizations.

12. An intelligent code differencing system employing code clone detection technology, comprising: a clone detection component configured to generate information indicative of a determination of a type of content change between at least two versions of source code; and

an importance engine configured to determine a level of importance associated with the type of the content change between the at least two versions of the source code and output information indicative of the level of importance.

13. The system of claim 12, wherein the level of importance is based, at least, on whether the type of the content change between the at least two versions of the source code is a change in format, a lexical level change or a logical change.

14. The system of claim 13, wherein the change in format corresponds to a first level of importance, the lexical change corresponds to a second level of importance and the logical change corresponds to a third level of importance, wherein the third level of importance is greater than the second level of importance and the second level of importance is greater than the first level of importance.

15. The system of claim 12, further comprising a characterization engine configured to determine a characterization of the type of content change between the at least two versions of source code, wherein the characterization includes semantic information.

16. The system of claim 15, wherein the importance engine is further configured to determine the level of importance based, at least, on the characterization.

17. The system of claim 16, wherein the importance engine is further configured to assign a value to the characterization.

18. The system of claim 17, wherein the value is a percentage.

19. The system of claim 16, wherein the characterization is at least one of a new code snippet or function, a duplicated code snippet or function, a moved function, a renamed function, a modified function, a deleted code snippet or function or a combination of moved and renamed function.

20. The system of claim 12, further comprising a visualization engine configured to generate and output a visualization of the information indicative of the level of importance.