CN111581104A - DevOps research and development operation integration-based measurement method - Google Patents
DevOps research and development operation integration-based measurement method Download PDFInfo
- Publication number
- CN111581104A CN111581104A CN202010394142.6A CN202010394142A CN111581104A CN 111581104 A CN111581104 A CN 111581104A CN 202010394142 A CN202010394142 A CN 202010394142A CN 111581104 A CN111581104 A CN 111581104A
- Authority
- CN
- China
- Prior art keywords
- data
- development
- measurement
- devops
- metrology
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000691 measurement method Methods 0.000 title claims abstract description 34
- 230000010354 integration Effects 0.000 title claims abstract description 24
- 238000012827 research and development Methods 0.000 title description 19
- 238000005259 measurement Methods 0.000 claims abstract description 74
- 238000011161 development Methods 0.000 claims abstract description 46
- 238000000034 method Methods 0.000 claims abstract description 43
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- 230000002159 abnormal effect Effects 0.000 claims abstract description 8
- 238000004140 cleaning Methods 0.000 claims abstract description 8
- 238000012360 testing method Methods 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 14
- 238000007726 management method Methods 0.000 claims description 12
- 230000004044 response Effects 0.000 claims description 7
- 238000012423 maintenance Methods 0.000 claims description 6
- 238000013461 design Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 4
- 230000006399 behavior Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 claims description 2
- 238000011160 research Methods 0.000 claims description 2
- 238000012216 screening Methods 0.000 abstract 1
- 238000012384 transportation and delivery Methods 0.000 description 8
- 230000007547 defect Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 230000036541 health Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000013070 change management Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000002085 persistent effect Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3604—Software analysis for verifying properties of programs
- G06F11/3612—Software analysis for verifying properties of programs by runtime analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Economics (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention provides a measurement method based on development and operation integration of DevOps, and relates to the field of measurement methods. A measurement method based on DevOps development and operation integration comprises the following steps: storing different metrology methods in a metrology model, thereby selecting the metrology method therein; collecting measurement data from a data source, cleaning and storing the measurement data; analyzing the metric data to extract feature data as metric metadata; and storing the metric metadata according to the data source, and analyzing all the stored metric metadata according to the selected metric method to obtain the development trend and the abnormal analysis of the metric data. The invention can be rapidly and completely collected and analyzed, thereby satisfying the comprehensive measurement requirement in the current DevOps practice, measuring software research and development team, individual role, software system and even business target, rapidly and completely collecting and analyzing, objectively displaying the relevant current status report and trend, and further being used as the important basis for organizing the decision reference of the manager, product manager, individual role and the like and timely screening bottleneck problem.
Description
Technical Field
The invention relates to the field of measurement methods, in particular to a measurement method based on development and operation integration of DevOps.
Background
With gradual landing and vigorous development of concepts such as DevOps, cloud computing, micro-service, container and the like in recent years, more and more machines are used, more and more applications are used, service contents are more and more subtle, and application operation basic environments are more and more diversified, such as containers, virtual machines and physical machines. First, in the face of such complex scenarios, the traditional measurement method focuses on the measurement of a single project such as software project management, development cost, software quality, development productivity (thousand lines of code per person/month). Secondly, in the whole software development life cycle, a large amount of development process data is generated from all links such as requirements, design, development, test, deployment, operation and maintenance, and the data should be fed back to hands of different roles at the highest speed. In the past, the data are abandoned or tied to a high place after being generated, the data are rarely used, or the data are only used at the end of the year in some enterprises, and the used scenes are not work reports, annual research and development big data display and the like. In the face of factors such as current complex business scenes, organizational structures, individual differences, working habits, system platforms and the like, how to objectively and truly measure software research and development teams, individual roles, software systems and even business targets has been rarely mentioned. Moreover, for whether the research and development big data scattered everywhere can be rapidly and completely collected and analyzed by adopting an advanced scheme, what kind of measurement model needs to be explored to be more suitable for the huge and complicated measurement requirements.
Disclosure of Invention
The invention aims to provide a measurement method based on development and operation integration of DevOps, which can measure software development teams, individual roles, software systems and even business targets, and can quickly and completely collect and analyze the software development teams, the individual roles and the software systems, thereby meeting the current measurement requirements.
The embodiment of the invention is realized by the following steps:
the embodiment of the application provides a measurement method based on development and operation integration of DevOps, which comprises the following steps: storing different measurement methods in the measurement model, thereby selecting the measurement method; collecting measurement data from a data source, cleaning and storing the measurement data; analyzing the metric data to extract feature data as metric metadata; and storing the measurement metadata according to the data source, and analyzing all the stored measurement metadata according to the selected measurement method to obtain the development trend and the abnormal analysis of the measurement data.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
1. according to the invention, the software development team, the individual role and the business target are respectively analyzed by different measurement methods, so that the measurement indexes of the new technologies such as DevOps, cloud computing, micro service, containers and the like after the vigorous development of the new technologies can be displayed, the research and development efficiency, the research and development quality, the research and development speed, the research and development health degree and the like can be displayed besides the measurement of the single project such as software project management, development cost, software quality and development productivity (thousand-line codes per month), the measurement indexes of the contents such as research and development efficiency, research and development quality, research and development speed and research and development health degree and the like can be displayed, the use under;
2. by acquiring, cleaning and storing measurement data from a data source, in the face of factors such as current complex business scenes, organizational structures, individual differences, working habits, system platforms and the like, the method can objectively and truly measure measurement indexes and visual displays of software research and development teams, individual roles, software systems and business targets, and is convenient for storing analysis records of the measurement data through big data, so that the accuracy of measurement analysis results is improved;
3. the invention can adopt a set of advanced schemes to rapidly and completely collect, analyze and display the research and development big data scattered in various places of the enterprise;
4. in the whole software development life cycle, a large amount of development process data is generated in all links such as requirements, design, development, test, deployment, operation, maintenance and the like, processed and fed back to hands of different roles at the highest speed, and the effects of problem occurrence, rapid detection, rapid feedback and fast solving of a benign closed loop are formed;
5. according to the comprehensive analysis of the current measurement metadata and the measurement metadata which is stored once, the measurement data can be conveniently and completely collected and analyzed, and the characteristic data of the measurement data is extracted, so that the characteristic data is stored as the measurement metadata to obtain the development trend and the abnormal analysis of the measurement data, and the method is suitable for the internal measurement requirements of huge and complicated software research and development enterprises and is also suitable for the measurement of IT internal systems or teams of various enterprises.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a developing and operating integrated metrology method based on DevOps in embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of drawing an engineer portrait in embodiment 1 of the present invention;
FIG. 3 is a schematic diagram of drawing a team representation according to embodiment 2 of the present invention;
FIG. 4 is a schematic diagram of drawing a system image according to embodiment 3 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer", and the like indicate orientations or positional relationships based on orientations or positional relationships shown in the drawings or orientations or positional relationships conventionally found in use of products of the application, and are used only for convenience in describing the present application and for simplification of description, but do not indicate or imply that the referred devices or elements must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present application.
In the description of the present application, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "disposed" and "connected" are to be interpreted broadly, e.g., as being either fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the individual features of the embodiments can be combined with one another without conflict.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a measurement method based on DevOps development and operation integration according to an embodiment of the present application. The DevOps-based integrated development and operation measurement method comprises the following steps: storing different measurement methods in the measurement model, thereby selecting the measurement method; collecting measurement data from a data source, cleaning and storing the measurement data; analyzing the metric data to extract feature data as metric metadata; and storing the measurement metadata according to the data source, and analyzing all the stored measurement metadata according to the selected measurement method to obtain the development trend and the abnormal analysis of the measurement data.
Optionally, when a suitable measurement method is selected by the measurement model, it is first necessary to specify the measurement object, so that a suitable measurement method is selected. Optionally, the metrology model comprises metrology methods for individuals, teams and systems, respectively. Optionally, risk assessment may also be performed on the anomaly analysis by analyzing all saved metric metadata. Optionally, when all the stored metric metadata are analyzed comprehensively, the corresponding metric metadata are searched through the stored metric data. The measurement method finds the development trend of the measurement data by finding a certain rule of the measurement metadata, and the stored measurement metadata is convenient for quickly finding abnormal data, thereby meeting the requirements of people on measurement technology.
The analysis metric data and metric metadata may be implemented by a processor, which may be an integrated circuit chip having signal processing capabilities. The Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
It is understood that the DevOps-based development and operation-integrated metrology method shown in fig. 1 is merely illustrative, and the DevOps-based development and operation-integrated metrology method can also include more or fewer steps or components than those shown in fig. 1, or have a different configuration than that shown in fig. 1. The steps shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
When the measurement method is applied to individuals, as shown in fig. 2, an engineer portrait is drawn by taking the measurement method of developing operation integration based on DevOps as an example, and measurement data of the engineer is collected by taking personal related data of the engineer as a data source. Wherein the data source may comprise one or more of expertise, ability, experience, and work ability of an engineer. So that the characteristic data of the engineer is analyzed according to the measurement data as measurement metadata, such as: managing experience, hobbies and code data. Wherein the code data comprises code quality data, code problem data, and code submission data. Optionally, the past measurement data of the engineer includes one or more of an engineer behavior trace, development process data, code analysis data, programming field data, and programming collaboration data. The feature data extracted from the metric data of the engineer in the past is, as metric metadata, for example: key talents, multiple fields, technical innovation, patent invention and research and development data. Future measurement data of the engineer, such as potential, management, technical expertise, work power, business and competency, may be analyzed based on the past measurement metadata and the current measurement metadata. Therefore, the future development trend of the engineer is predicted, and potential employees are explored to manage the reserve talents. Meanwhile, code defect detection can be carried out through data related to codes such as past development process data, code analysis data, programming field data and programming cooperation data of engineers and code data such as current code quality data, problem data and code submission data of engineers, so that code defects are automatically corrected, and relevant codes are completed or generated.
Optionally, the measurement data may include one or more of a business index, an application index, a system software monitoring index, a system index, a research and development process index, a pipeline index, role behavior data, research and development cooperation data, external cooperation data, and human resource management data. Different measurement models can be established through various types of measurement data, so that the measurement metadata are analyzed according to a measurement method to obtain the development trend of different measurement data, and the abnormity of the measurement metadata and the risk of the measurement metadata are analyzed and evaluated.
Optionally, the application metrics may include one or more of availability, exceptions, throughput, response time, current number of waiting pens, resource occupancy, amount of requests, log size, performance, queue depth, number of threads, number of service calls, amount of access, and service availability. Wherein, the current waiting number can be understood as the traffic needing queuing processing currently.
Optionally, the service index may include one or more of a large amount of flow, a flow area, a flow detail, a number of requests, a response time, and a number of responses.
Optionally, the system index may include one or more of a CPU load, a memory load, a disk load, a network IO, a disk IO, a tcp connection number, and a process number.
Optionally, the data source may include one or more of product target data, project data, demand management, code data, test data, build data, deployment data, operation and maintenance data, monitoring data, developer data, tester data, operation and maintenance personnel data, and basic design environment data.
Alternatively, the metric data may be stored by one or more of a file system, an indexing system, a message queue, a database, a data warehouse, and a data lake.
Optionally, the cleansing of the metrology data may include cleansing one or more of anomaly data, cheating data, and testing data.
Optionally, the metric data may be analyzed by one or more of MapReduce, log retrieval, feature extraction, sample extraction, index calculation, trend prediction, anomaly analysis, and risk prediction to extract the feature data. The MapReduce realizes reliability by distributing large-scale operation on a data set to each node on the network, and each node periodically returns the work completed by the node and the latest state. If a node remains silent for more than a predetermined time interval, the master node records the status of the node as dead and sends the data assigned to the node to another node.
Optionally, the measurement data is displayed through one or more of a cockpit report, a quality monthly report, a monthly report annual report, a PC terminal, a mobile terminal, and a notification subscription. The cockpit report forms show key indexes of the service data through four components, namely texts, pictures, tables and statistical graphs, can visually monitor operation conditions, and can perform early warning and mining analysis on abnormal key indexes in time.
Example 2
The embodiment of the present application is different from embodiment 1 in that, when the measurement method is used for team imaging, please refer to fig. 3, taking the example of drawing a team image based on the measurement method of DevOps research and development and operation integration, when a team includes a plurality of engineers in embodiment 1, the measurement data analysis and prediction of each engineer is performed through the steps in embodiment 1, so as to summarize the team imaging. The team image is mainly based on data of each person in the team and combined with relevant data of a team bearing project to generate a team image.
Optionally, the metric data may include one or more of delivery performance, delivery quality, delivery speed, business value, health, maturity, and the like. The delivery efficiency data can be expressed by indexes such as research and development productivity, production stability, iteration speed, per-capita functional point output and the like. The delivery quality data can be represented by indexes such as required quality, technical debt rate, thousand-line code defect rate, development/integration test/system test/production defect escape rate, defect trend, online change failure rate, serious production accident number, defect discovery proportion of users, system/application downtime duration and the like. The delivery speed data can be represented by indexes such as a required delivery cycle, a required completion degree, a required response rate, the stay time of the requirement in each stage, throughput, defect average diagnosis/repair/recovery time, construction (compiling/single testing/scanning) speed, online change time and the like. The business value data can be expressed by indexes such as user value output rate, net recommended value, function acceptance rate, demand online rework rate, demand flow efficiency and the like. The health degree data can be represented by indexes such as development/test environment generation time, red light (problems such as development and test) restoration time, process conformity and the like. The maturity can be obtained by comprehensively calculating the various indexes according to a measurement model.
Example 3
Different from embodiment 1, in the embodiment of the present application, when the metrology method is applied to the system portrait, please refer to fig. 4, which takes the example of drawing the system portrait of software development based on the metrology method of DevOps research and development and operation integration. The method comprises the steps of extracting version control data, changing management data, continuously integrating and constructing data, testing related data, quality entrance guard data, deploying pipelines, environment management data, database change related data, system operation related data and service data by combining related processes and tool chains related to requirement stages in a software development life cycle, such as development, entrance guard, testing, release, commissioning and the like, and forming related data of a software system together to acquire measurement data. The image of the item a and the image of the item B are displayed in superposition, and for the sake of convenience of distinction, the images of the item a and the item B are respectively marked.
Optionally, the system image data may include one or more of development management, requirements activity, version control, requirements change management, code quality management, persistent integration, test layering policy, automation test, deployment and release management, persistent delivery pipeline, test data management, database change management, event and change management, and the like.
Optionally, the system portrait class of data may contain or reuse one or more of the team portrait data.
Optionally, the system index may include one or more of a CPU load, a memory load, a disk load, a network IO, a disk IO, a tcp connection number, and a process number.
In summary, the measurement method based on DevOps research, development and operation integration stores different measurement methods through measurement models, and meets the requirement of use in different use environments; by selecting a proper measurement method, a measurement target is definite, so that the measurement of different data sources is convenient to distinguish; by collecting, cleaning and storing the measurement data from the data source, the measurement data can be conveniently and completely recorded through big data; the characteristic data is extracted through the measurement data to serve as measurement metadata, and the measurement metadata is stored according to the quantitative data, so that the measurement metadata can be conveniently searched and analyzed, and the accuracy of data measurement is improved; the measurement metadata of each data source is analyzed through a measurement method, so that the development trend of the measurement data is obtained, abnormal contents in the measurement data are analyzed through the rules of all the contents, and the data analysis requirement of the measurement technology is met.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The functions shown in the method may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A measurement method for developing and operating integration based on DevOps is characterized by comprising the following steps: storing different metrology methods in a metrology model, thereby selecting the metrology method therein; collecting measurement data from a data source, cleaning and storing the measurement data; analyzing the metric data to extract feature data as metric metadata; and storing the metric metadata according to the data source, and analyzing all the stored metric metadata according to the selected metric method to obtain the development trend and the abnormal analysis of the metric data.
2. The DevOps development and operation integration based metrology method as claimed in claim 1, wherein the metrology data comprises one or more of business metrics, application metrics, system software monitoring metrics, system metrics, development process metrics, pipeline metrics, role behavior data, development collaboration data, outsource data, human resources management data.
3. The DevOps-based development and operation integration based measurement method as claimed in claim 2, wherein the application metrics comprise one or more of availability, exception, throughput, response time, current number of waiting pens, resource occupancy, amount of requests, log size, performance, queue depth, number of threads, number of service calls, amount of access and service availability.
4. The DevOps-based development and operation integration based metrology method of claim 2 wherein the business metrics comprise one or more of large flow, flow region, flow detail, number of request strokes, response time and number of response strokes.
5. The DevOps development and operation integration-based metrology method of claim 2, wherein the system metrics comprise one or more of CPU load, memory load, disk load, network IO, disk IO, tcp connection count and process count.
6. The DevOps-based development and operation integration measurement method as claimed in claim 1 or 2, wherein the data source comprises one or more of product target data, project data, demand management, code data, test data, build data, deployment data, operation and maintenance data, monitoring data, developer data, tester data, operation and maintenance personnel data and infrastructure design environment data.
7. The DevOps-based development and operation integration based metrology method of claim 1 wherein the metrology data is stored by one or more of file system, indexing system, message queue, database, data warehouse, and data lake.
8. The DevOps development and operation integration based metrology method as claimed in claim 1, wherein the cleaning of metrology data comprises cleaning one or more of anomaly data, cheating data and testing data.
9. The DevOps research and operation integration based metrology method of claim 1, wherein the metrology data is analyzed by one or more of MapReduce, log retrieval, feature extraction, sample extraction, index calculation, trend prediction, anomaly analysis, and risk prediction to extract the feature data.
10. The DevOps development and operation integration-based metrology method as claimed in claim 1, wherein the metrology data is exposed through one or more of cockpit reports, quality monthly reports, yearly monthly reports, PC side, mobile side and notification subscription.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010394142.6A CN111581104A (en) | 2020-05-11 | 2020-05-11 | DevOps research and development operation integration-based measurement method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010394142.6A CN111581104A (en) | 2020-05-11 | 2020-05-11 | DevOps research and development operation integration-based measurement method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111581104A true CN111581104A (en) | 2020-08-25 |
Family
ID=72122967
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010394142.6A Pending CN111581104A (en) | 2020-05-11 | 2020-05-11 | DevOps research and development operation integration-based measurement method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111581104A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112465363A (en) * | 2020-12-03 | 2021-03-09 | 合肥天源迪科信息技术有限公司 | Task management platform and method |
| CN112559645A (en) * | 2020-12-25 | 2021-03-26 | 中国农业银行股份有限公司 | Processing method and device for mass operation and maintenance data |
| CN113065737A (en) * | 2021-02-04 | 2021-07-02 | 上海金融期货信息技术有限公司 | DevOps-based efficiency measurement method and system |
| CN113204334A (en) * | 2021-05-25 | 2021-08-03 | 郭淙苌 | Git-based software research and development efficiency measurement system |
| CN113222300A (en) * | 2021-06-15 | 2021-08-06 | 中国银行股份有限公司 | Method and device for processing product modification data, readable medium and equipment |
| CN113780986A (en) * | 2021-08-26 | 2021-12-10 | 济南浪潮数据技术有限公司 | Measurement method, system, equipment and medium for software development process |
| US11847539B2 (en) | 2021-07-08 | 2023-12-19 | International Business Machines Corporation | Deploy build blocks and analyze build bottleneck |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008036921A2 (en) * | 2006-09-21 | 2008-03-27 | Impact Technologies, Llc | Systems and methods for predicting failure of electronic systems and assessing level of degradation and remaining useful life |
| US20160335260A1 (en) * | 2015-05-11 | 2016-11-17 | Informatica Llc | Metric Recommendations in an Event Log Analytics Environment |
| CN107423911A (en) * | 2017-08-02 | 2017-12-01 | 中国科学院上海高等研究院 | Software Evaluating Degree of Success method/system, computer-readable recording medium and equipment |
| CN108595983A (en) * | 2018-04-24 | 2018-09-28 | 许昌学院 | A kind of hardware structure and application context integrity measurement method based on hardware security isolated execution environment |
| CN110569197A (en) * | 2019-09-12 | 2019-12-13 | 中国工程物理研究院计算机应用研究所 | Credible evidence analysis and measurement method for software credibility evaluation |
-
2020
- 2020-05-11 CN CN202010394142.6A patent/CN111581104A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008036921A2 (en) * | 2006-09-21 | 2008-03-27 | Impact Technologies, Llc | Systems and methods for predicting failure of electronic systems and assessing level of degradation and remaining useful life |
| US20160335260A1 (en) * | 2015-05-11 | 2016-11-17 | Informatica Llc | Metric Recommendations in an Event Log Analytics Environment |
| CN107423911A (en) * | 2017-08-02 | 2017-12-01 | 中国科学院上海高等研究院 | Software Evaluating Degree of Success method/system, computer-readable recording medium and equipment |
| CN108595983A (en) * | 2018-04-24 | 2018-09-28 | 许昌学院 | A kind of hardware structure and application context integrity measurement method based on hardware security isolated execution environment |
| CN110569197A (en) * | 2019-09-12 | 2019-12-13 | 中国工程物理研究院计算机应用研究所 | Credible evidence analysis and measurement method for software credibility evaluation |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112465363A (en) * | 2020-12-03 | 2021-03-09 | 合肥天源迪科信息技术有限公司 | Task management platform and method |
| CN112465363B (en) * | 2020-12-03 | 2024-04-16 | 合肥天源迪科信息技术有限公司 | Task management platform and method |
| CN112559645A (en) * | 2020-12-25 | 2021-03-26 | 中国农业银行股份有限公司 | Processing method and device for mass operation and maintenance data |
| CN113065737A (en) * | 2021-02-04 | 2021-07-02 | 上海金融期货信息技术有限公司 | DevOps-based efficiency measurement method and system |
| CN113204334A (en) * | 2021-05-25 | 2021-08-03 | 郭淙苌 | Git-based software research and development efficiency measurement system |
| CN113222300A (en) * | 2021-06-15 | 2021-08-06 | 中国银行股份有限公司 | Method and device for processing product modification data, readable medium and equipment |
| US11847539B2 (en) | 2021-07-08 | 2023-12-19 | International Business Machines Corporation | Deploy build blocks and analyze build bottleneck |
| CN113780986A (en) * | 2021-08-26 | 2021-12-10 | 济南浪潮数据技术有限公司 | Measurement method, system, equipment and medium for software development process |
| CN113780986B (en) * | 2021-08-26 | 2024-02-27 | 济南浪潮数据技术有限公司 | Measurement method, system, equipment and medium for software research and development process |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111581104A (en) | DevOps research and development operation integration-based measurement method | |
| Zhang et al. | Visual analytics for the big data era—A comparative review of state-of-the-art commercial systems | |
| US20190188616A1 (en) | Risk simulation and assessment tool | |
| US20170109657A1 (en) | Machine Learning-Based Model for Identifying Executions of a Business Process | |
| US20170109676A1 (en) | Generation of Candidate Sequences Using Links Between Nonconsecutively Performed Steps of a Business Process | |
| CN105868373B (en) | Method and device for processing key data of power business information system | |
| US11321327B2 (en) | Intelligence situational awareness | |
| US20170109668A1 (en) | Model for Linking Between Nonconsecutively Performed Steps in a Business Process | |
| CN107908550B (en) | Software defect statistical processing method and device | |
| US20170109667A1 (en) | Automaton-Based Identification of Executions of a Business Process | |
| US20170109636A1 (en) | Crowd-Based Model for Identifying Executions of a Business Process | |
| US20190228353A1 (en) | Competition-based tool for anomaly detection of business process time series in it environments | |
| Koru et al. | Testing the theory of relative defect proneness for closed-source software | |
| Hammad et al. | Provenance as a service: A data-centric approach for real-time monitoring | |
| CA3170083C (en) | Systems and methods for project and program management using artificial intelligence | |
| Feitosa et al. | The evolution of design pattern grime: An industrial case study | |
| CN114303134A (en) | Method, apparatus and computer readable medium for maintaining visual consistency | |
| Özdağoğlu et al. | Monitoring the software bug‐fixing process through the process mining approach | |
| Chircu et al. | Visualization and machine learning for data center management | |
| CN114500316B (en) | Method and system for inspecting equipment of Internet of things | |
| EP4174740A1 (en) | Generating efficiency metrics for knowledge workers | |
| Sureddy et al. | A Framework for Monitoring Data Warehousing Applications | |
| US20140350995A1 (en) | Characterizing Statistical Time-Bounded Incident Management Systems | |
| US9928152B2 (en) | Computer implemented system and method to non-intrusive sensing and instrumentation of work process | |
| US20220230121A1 (en) | Modeling Expectation Mismatches |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20221021 Address after: 100000 Room 2199, Floor 2, No. 11, Shixing East Street, Shijingshan District, Beijing Applicant after: Beijing Huayou Technology Co.,Ltd. Address before: 2206, Block A, Wangjing Fangheng International Center, Chaoyang District, Beijing 100102 Applicant before: Lei Tao |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200825 |
|
| RJ01 | Rejection of invention patent application after publication |