CN113297092A - Defect prediction method of software and related equipment - Google Patents
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Abstract
The invention provides a software defect prediction method and related equipment, wherein the method comprises the following steps: acquiring defects of software to be tested in the running process; acquiring the data dimension of a database corresponding to the software to be tested; classifying the defects according to the data dimension to obtain various first input elements and first numerical values corresponding to the first input elements; and inputting the various first input elements and first values corresponding to the first input elements into a prediction model, and predicting to obtain the quantity distribution of the software to be tested on each defect grade. The method of the invention improves the testing efficiency and the testing quality of the software.
Description
Technical Field
The present invention relates to software research technologies, and in particular, to a method and related device for predicting software defects.
Background
Software defects are inherent properties of software and are "by-products" of the software development process. The main hazards are to affect the quality of the software, extend the development period and increase the development cost. The staged test is an important means for finding software errors in time and improving the software quality, and the accurate prediction of the distribution condition of the software defects has important guiding significance for the software testing work.
With the continuous development of computer technology, the scale and complexity of software increase in a geometric progression, and more influencing factors need to be analyzed in order to accurately and finely predict the generation and distribution conditions of software defects.
In the prior art, a tester tests various aspects of software according to a test flow to obtain software defects, but the software defects in some aspects are rarely or even not set, and the test in the aspects is not a necessary test flow; therefore, testing various aspects of the software according to the testing process results in low testing efficiency and testing quality of the software.
Disclosure of Invention
The invention provides a software defect prediction method and related equipment, which are used for solving the problems of low software testing efficiency and low software testing quality.
In one aspect, the present invention provides a method for predicting software defects, including:
acquiring defects of software to be tested in the running process;
acquiring data dimensions of a database corresponding to the software to be tested, wherein the data dimensions comprise at least two of functional requirements, performance requirements, module numbers, module complexity, module service priorities, codes and test cases;
classifying the defects according to the data dimension to obtain various first input elements and first numerical values corresponding to the first input elements;
and inputting the various first input elements and first values corresponding to the first input elements into a prediction model, and predicting to obtain the quantity distribution of the software to be tested on each defect grade.
In one embodiment, the step of inputting each type of the first input element and the first numerical value corresponding to the first input element to the prediction model includes:
constructing a feature vector according to the various first input elements and first numerical values corresponding to the first input elements;
inputting the feature vector to the predictive model.
In an embodiment, before the step of inputting each type of the first input element and the first numerical value corresponding to the first input element to the prediction model, the method further includes:
acquiring the defects of each piece of training software and the quantity distribution of each piece of training software on each defect grade;
determining various second input elements and second numerical values corresponding to the second input elements according to the defects of the training software;
determining a training sample corresponding to the training software according to the second input elements, the second numerical values and the quantity distribution of the training samples on each defect grade corresponding to the training software;
and training the prediction model according to each training sample.
In one embodiment, the prediction model comprises a hidden layer and an output layer, and the hidden layer and the output layer are both provided with excitation functions;
the excitation function σ (x) ═ max (-0.01x, x) -0.01, where x is the input value to the prediction model.
In an embodiment, the data dimension includes at least two of functionality requirements, performance requirements, module number, module complexity, module business priority, code, and test cases.
In one embodiment, the defect levels include at least two of a fatal defect, a serious defect, a general defect, and a light defect.
In another aspect, the present invention further provides a software defect prediction apparatus, including:
the acquisition module is used for acquiring the defects of the software to be detected in the running process;
the acquisition module is further configured to acquire data dimensions of a database corresponding to the software to be tested, where the data dimensions include at least two of functionality requirements, performance requirements, module numbers, module complexity, module service priorities, codes, and test cases;
the classification module is used for classifying the defects according to the data dimensions to obtain various first input elements and first numerical values corresponding to the first input elements;
and the input module is used for inputting various first input elements and first values corresponding to the first input elements into a prediction model, and predicting to obtain the quantity distribution of the software to be tested on each defect grade.
In another aspect, the present invention provides a defect prediction apparatus for software, including: a memory and a processor;
the memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored by the memory, causing the processor to perform the method for defect prediction of software as described above.
In another aspect, the present invention provides a computer-readable storage medium having stored therein computer-executable instructions for implementing the method for defect prediction of software as described above when executed by a processor.
In another aspect, the present invention provides a computer program product comprising a computer program which, when executed by a processor, implements a method of defect prediction for software as described above.
According to the software defect prediction method and the related equipment, firstly, defects of the software to be detected in the operation process and the data dimension of the database are obtained, each defect is classified according to the data dimension to obtain various first input elements and first values corresponding to the first input elements, and then the first input elements and the first values are input into the prediction model, so that the quantity distribution of the software to be detected on each defect level is obtained through prediction. According to the invention, the quantity distribution of the software on each defect level is predicted through the defects of the software in the previous running process, so that a tester can adjust the test flow of the software to be tested based on the predicted quantity distribution of the defect levels, and the test efficiency and the test quality of the software are improved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a system architecture diagram of a fault prediction method implementing the software of the present invention;
FIG. 2 is a flowchart illustrating a method for predicting defects in software according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a step S40 of a second embodiment of the method for predicting defects of software according to the present invention;
FIG. 4 is a flowchart illustrating a method for predicting defects in software according to a third embodiment of the present invention;
FIG. 5 is a schematic diagram of a neural network in the prediction model of the present invention;
FIG. 6 is a schematic diagram of another structure of a neural network in the prediction model of the present invention;
FIG. 7 is a hardware block diagram of a software defect prediction apparatus according to the present invention;
FIG. 8 is a functional block diagram of a defect prediction apparatus of the software according to the present invention.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The present invention provides a software defect prediction method, which can be implemented by a system architecture diagram shown in fig. 1, fig. 1 is a system architecture diagram for implementing the software defect prediction method of the present invention, 101 in fig. 1 is a software defect prediction apparatus, and the software defect prediction apparatus 101 can be a device with data processing capability, such as a computer, a server, a mobile terminal, and the like. As shown in fig. 1, a defect prediction apparatus 101 of the software is connected to a server 102 provided with a database via network communication. The database in the server 102 stores the defects found during the running process of the software, and each defect of the software constitutes a data set, and the data set is stored in the database in association with the identifier of the software. The software defect prediction device 101 accesses the server 102, obtains each defect of the software to be tested in the operation process from the database of the server 102, and predicts the quantity distribution of the software to be tested on each defect level according to each defect.
The following describes the technical solutions of the present invention and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Referring to fig. 2, fig. 2 is a first embodiment of a method for predicting a defect of software according to the present invention, and the method for predicting a defect of software includes the following steps:
and step S10, acquiring the defects of the software to be tested in the running process.
In the present embodiment, the defect prediction apparatus whose main body is software is executed, and for convenience of description, the apparatus is referred to as a defect prediction apparatus of software hereinafter. The device is provided with a database, and the database stores the defects found in the running process of each software. The running process of the software can be normal running of the software or test running of the software. The defect set D corresponding to each software in the database is { Ri, i is 1,2,3 …, n }, where Ri is an element of the data set D, or is a record in the software running process corresponding to the data set, and the record includes defects found in the software running process. The element may be a vector and the dimensions of each element in the set D are the same. The dimensions of the vector are determined by the data dimensions of the database. Specifically, the types of defects can be divided into requirements, systems, modules, codes and test cases according to the requirements, and each type corresponds to one data dimension of the database. In addition, the types can be classified in a more detailed manner, for example, requirements are subdivided into functional requirements and performance requirements; the modules are subdivided into module numbers, module complexity, and module business priority, and thus, the data dimensions of the database include at least two of functionality requirements, performance requirements, module numbers, module complexity, module business priority, code, and test cases. It should be noted that all record dimensions should remain consistent and that once the database is built, the data dimensions do not change.
The data set D is stored in association with the identification of the software in the database. When the device determines the software to be tested, namely the software to be tested, the corresponding data set is determined from the database based on the identification of the software to be tested, and then the defects of the software to be tested in the running process are obtained from the data set.
And step S20, acquiring the data dimension of the database corresponding to the software to be tested.
And step S30, classifying the defects according to the data dimensions to obtain various first input elements and first values corresponding to the first input elements.
After the device obtains the defects of the software to be tested in the operation process, the defects are required to be classified correspondingly. Specifically, the data dimension in the database is an attribute element after defect classification, that is, at least two of functional requirements, performance requirements, module numbers, module complexity, module service priorities, codes and test cases can be obtained by classifying defects.
The data dimension of the database can be determined according to a conventional software engineering standard, and the conventional software engineering standard can be an ISO standard or a national standard. The device classifies each defect based on the data dimension, and accordingly, each first input element and a first numerical value corresponding to the first data element are obtained. The first input element is the type of the classified defect, and the first value is the number of the defects in the first input element.
And step S40, inputting various first input elements and first values corresponding to the first input elements into the prediction model, and predicting to obtain the quantity distribution of the software to be tested on each defect level.
After the device obtains each first input element and the first numerical value corresponding to the input element, each first data element and the first numerical value corresponding to the first input element are input into the prediction model, and data output by the prediction model is the quantity distribution of the software to be tested on each defect level.
The defect grade includes at least two of a fatal defect, a serious defect, a general defect, and a light defect. Each defect level will be explained below.
1) Fatal defect: the defects of system crash, dead halt, dead loop, database data loss, connection error with the database, main function loss, basic module loss and the like are caused.
2) Serious defects are as follows: the defects that the main functions of the system are partially lost, the database is saved and called wrongly, the user data is lost, the function menu cannot be used but the test of other functions is not influenced, the function design is seriously inconsistent with the requirement, the module cannot be started or called, the program is restarted, the automatic exit is realized, the calling conflict among the associated programs is caused, the safety problem, the stability and the like are overcome.
3) General disadvantages: the defects that the function is not completely realized but the use is not influenced, the function menu has defects but the system stability is not influenced, and the like are indicated.
4) Slight defects: interface or performance deficiencies, proposing a class of problems, not affecting the execution of operational functions, solutions that can optimize performance, and the like. Such as: wrongly written characters, irregular interface formats, overlapped page display, hidden display failure, unclear description, lost prompt words, irregular character arrangement, incorrect cursor position, poor user experience feeling, schemes capable of optimizing performance and the like.
The device inputs a first input element in a prediction model: (requirement 1, system a, module a.13, code file b.1.2, test case c.3.4), data output by the prediction model are obtained: (3 pieces of fatal defect, 1 piece of serious defect, 3 pieces of general defect, and 10 pieces of slight defect). It should be noted that requirement 1, system a, module a.13, code file b.1.2, and test case c.3.4 refer to specific first input elements, and requirement 1, system a, module a.13, code file b.1.2, and test case c.3.4 have corresponding first values, which are not labeled for convenience of description.
In the technical scheme provided by this embodiment, firstly, the defects of the software to be tested in the operation process and the data dimensions of the database are obtained, and each defect is classified according to the data dimensions to obtain various first input elements and first values corresponding to the first input elements, and then the first input elements and the first values are input to the prediction model, so that the quantity distribution of the software to be tested on each defect level is obtained through prediction. According to the invention, the quantity distribution of the software on each defect level is predicted through the defects of the software in the previous running process, so that a tester can adjust the test flow of the software to be tested based on the predicted quantity distribution of the defect levels, and the test efficiency and the test quality of the software are improved.
Referring to fig. 3, fig. 3 is a second embodiment of the method for predicting the defect of the software according to the present invention, and based on the first embodiment, step S40 includes:
step S41, constructing a feature vector according to the various first input elements and the first values corresponding to the first input elements.
In step S42, the feature vector is input to the prediction model.
In this embodiment, records of the software to be tested in the database are vectors, that is, when the software to be tested is operated or tested each time, the database constructs defects found in the operation process into a feature vector according to data dimensions, and the dimensions of the feature vector are the same as the data dimensions of the database.
In this regard, the device constructs a feature vector based on each of the first input elements and the first numerical value after obtaining each of the first input elements and the first numerical value corresponding to the first input element, and stores the feature vector in the database. The device inputs the characteristic vectors into the prediction model, and the prediction model outputs the quantity distribution of the software to be tested on each defect grade.
It should be noted that, if records in the data set in the database are feature vectors, numerical values corresponding to the same dimension and having the same features in the data set may be superimposed, so that a feature vector is constructed based on the superimposed numerical values and the corresponding dimension reproduction. It can be understood that, at this time, the defect of the software to be tested in the operation process is embodied by each feature vector, that is, the device performs addition operation on the numerical values of the same dimension of each feature vector to obtain various first input elements and first numerical values corresponding to the first input elements, and constructs the feature vector of the input prediction model based on each first input element and the first numerical values.
In the technical scheme provided by this embodiment, the device constructs the feature vector based on various first input elements and the first numerical values corresponding to the first input elements, and inputs the feature vector into the prediction model, so as to rapidly obtain the quantity distribution of the software to be tested on each defect level.
Referring to fig. 4, fig. 4 is a diagram illustrating a method for predicting defects of software according to a third embodiment of the present invention, which further includes, based on the first or second real-time exchange rate, before step S10:
step S50, acquiring the defects of each training software and the number distribution of each training software on each defect level.
And step S60, determining various second input elements and second values corresponding to the second input elements according to the defects of the training software.
And step S70, determining the training samples corresponding to the training software according to the various second input elements, the second numerical values and the quantity distribution of the training samples on the defect levels corresponding to the training software.
In step S80, the prediction model is trained based on each training sample.
Before prediction is performed by using the prediction model, the prediction model needs to be trained, that is, before step S10, the prediction model needs to be trained. Of course, the training of the prediction model may be prior to steps S40, S30, and S20. Specifically, the device acquires the defects of each piece of training software and the quantity distribution of the training software on each defect level. The training software refers to software for which the distribution of the number on the defect level has been known, as sample software for training the prediction model. The device determines various second input elements and second numerical values corresponding to the second input elements according to the defects of the training software, and determines the training samples corresponding to the training software according to the second input elements, the second numerical values and the quantity distribution, for example, a feature vector is constructed based on the second input elements and the second numerical values corresponding to the second input elements, and the quantity distribution is used as a label of the feature vector, so that the training samples corresponding to the training software can be obtained.
The device can obtain a plurality of training samples in this way, and trains the prediction model through the plurality of training samples, so that the parameters of the neural network in the prediction model are adjusted, and the prediction model can accurately predict the quantity distribution of the software to be tested on each defect grade.
The prediction model comprises a neural network classifier, the neural network classifier consists of an input layer, an output layer and a hidden layer, each layer comprises a plurality of nodes called neurons, and the neurons and the connections among the neurons form a neural network; wherein the input layer and the output layer are composed of the input parameters and the output parameters. For example, a single hidden layer neural network is shown in fig. 1, which comprises three input layer nodes X1, X2, 1, one output layer node y, and three hidden layer nodes n1, n2, n3, which are connected as shown in fig. 5. It can be seen that if the stimulus function is not added, then each node is a linear combination of its predecessors:
y＝w2-1(w1-11x1+w1-21x2+b1-1)+w2-2(w1-12x1+w1-22x2+b1-2)+w2-3(w1-13x1+w1-23x2+b1-3)
in this case, the neural network classifier cannot accurately classify the non-linearly separable data set. In this regard, this problem can be solved by adding an excitation function to both the hidden layer and the output layer. Referring to fig. 6, both the hidden layer and the output layer of the prediction model are provided with excitation functions; and the excitation function σ (x) ═ max (-0.01x, x) -0.01, x ∈ R, where x is the input value of the prediction model, R is the record in the database, and R is the feature vector in the database. The design formula of the excitation function is equivalent to:
after adding the excitation function, the output y of the neural network becomes:
a1＝w1-11x1+w1-21x2+b1-1
a2＝w1-12x1+w1-22x2+b1-2
a3＝w1-13x1+w1-23x2+b1-3
y＝σ(w2-1σ(a1)+w2-2σ(a2)+w2-3σ(a3))
the excitation function is an improvement of a classical ReLU excitation function, and by designing the excitation function, the efficiency and the performance of a neural network can be effectively considered, so that a better identification effect is achieved.
In the technical scheme provided by this embodiment, before the device predicts the defects of the software to be tested, the device trains the prediction model through the training samples, so that the trained prediction model can accurately predict the number distribution of the software to be tested on each defect level.
Fig. 7 is a block diagram illustrating a software defect prediction apparatus, which may be a computer, a tablet device, a server, etc., according to an example embodiment.
The defect prediction apparatus 700 of software may include: a processor 71, e.g. a CPU, a memory 72. Those skilled in the art will appreciate that the configuration shown in FIG. 7 does not constitute a limitation of the software bug prediction device, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components. The memory 72 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The processor 71 may call a computer program stored in the memory 72 to perform all or part of the steps of the software defect prediction method described above.
A non-transitory computer readable storage medium, instructions in which, when executed by a processor of a terminal device, enable the terminal device to perform the method of defect prediction of software described above.
A computer program product comprising a computer program which, when executed by a processor of a terminal device, enables the terminal device to carry out the method of defect prediction of software as described above.
The present invention also provides a software defect prediction apparatus 800, including:
an obtaining module 801, configured to obtain a defect of software to be tested in an operation process;
an obtaining module 801, configured to obtain a data dimension of a database corresponding to software to be tested;
the classification module 802 is configured to classify each defect according to a data dimension to obtain various first input elements and first values corresponding to the first input elements;
the input module 803 is configured to input various first input elements and first values corresponding to the first input elements to the prediction model, and predict number distribution of the software to be tested at each defect level.
In one embodiment, the apparatus 800 for predicting defects of software includes:
the construction module is used for constructing a feature vector according to various first input elements and first numerical values corresponding to the first input elements;
an input module 803 is used for inputting the feature vector to the prediction model.
In one embodiment, the apparatus 800 for predicting defects of software includes:
an obtaining module 801, configured to obtain the defects of each piece of training software and the number distribution of each piece of training software on each defect level;
the determining module is used for determining various second input elements and second numerical values corresponding to the second input elements according to the defects of the training software;
the determining module is used for determining the training samples corresponding to the training software according to various second input elements and second numerical values corresponding to the training software and the quantity distribution of the training samples on each defect grade;
and the training module is used for training the prediction model according to each training sample.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for predicting a defect of software, comprising:
acquiring defects of software to be tested in the running process;
acquiring the data dimension of a database corresponding to the software to be tested;
classifying the defects according to the data dimension to obtain various first input elements and first numerical values corresponding to the first input elements;
and inputting the various first input elements and first values corresponding to the first input elements into a prediction model, and predicting to obtain the quantity distribution of the software to be tested on each defect grade.
2. The method of claim 1, wherein the step of inputting the types of the first input elements and the first values corresponding to the first input elements into the prediction model comprises:
constructing a feature vector according to the various first input elements and first numerical values corresponding to the first input elements; inputting the feature vector to the predictive model.
3. The method of claim 1, wherein before the step of inputting the types of the first input elements and the first values corresponding to the first input elements into the prediction model, the method further comprises:
acquiring the defects of each piece of training software and the quantity distribution of each piece of training software on each defect grade;
determining various second input elements and second numerical values corresponding to the second input elements according to the defects of the training software;
determining a training sample corresponding to the training software according to the second input elements, the second numerical values and the quantity distribution of the training samples on each defect grade corresponding to the training software;
and training the prediction model according to each training sample.
4. The method of claim 1, wherein the prediction model comprises a hidden layer and an output layer, and the hidden layer and the output layer are provided with excitation functions;
the excitation function σ (x) ═ max (-0 · 01x, x) -0.01, where x is the input value to the prediction model.
5. The method of any of claims 1-4, wherein the data dimensions include at least two of functional requirements, performance requirements, module number, module complexity, module business priority, code, and test cases.
6. The method of any one of claims 1 to 4, wherein the defect levels include at least two of fatal defects, severe defects, general defects, and light defects.
7. A software defect prediction apparatus, comprising:
the acquisition module is used for acquiring the defects of the software to be detected in the running process;
the acquisition module is further configured to acquire data dimensions of a database corresponding to the software to be tested, where the data dimensions include at least two of functionality requirements, performance requirements, module numbers, module complexity, module service priorities, codes, and test cases;
the classification module is used for classifying the defects according to the data dimensions to obtain various first input elements and first numerical values corresponding to the first input elements;
and the input module is used for inputting various first input elements and first values corresponding to the first input elements into a prediction model, and predicting to obtain the quantity distribution of the software to be tested on each defect grade.
8. A defect prediction apparatus for software, characterized by comprising: a memory and a processor;
the memory stores computer-executable instructions;
the processor executing the computer-executable instructions stored by the memory causes the processor to perform a method of defect prediction of software according to any of claims 1 to 6.
9. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, implement a method of defect prediction for software according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements a method of defect prediction for software according to any of claims 1 to 6.
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