Forward slicing algorithm based test data generation

Test data generation is one of the most technically challenging steps of testing software, but most commercial systems currently incorporate very little automation for this step. This paper presents results from a project that is trying to find ways to incorporate test data generation into practical test processes. The results include a new procedure for automatically generating test data that incorporates ideas from symbolic evaluation, constraint-based testing, and dynamic test data generation. It takes an initial set of values for each input, and dynamically ‘pushes’ the values through the control-flow graph of the program, modifying the sets of values as branches in the program are taken. The result is usually a set of values for each input parameter that has the property that any choice from the sets will cause the path to be traversed. This procedure uses new analysis techniques, offers improvements over previous research results in constraint-based testing, and combines several steps into one coherent process. The dynamic nature of this procedure yields several benefits. Moving through the control flow graph dynamically allows path constraints to be resolved immediately, which is more efficient both in space and time, and more often successful than constraint-based testing. This new procedure also incorporates an intelligent search technique based on bisection. The dynamic nature of this procedure also allows certain improvements to be made in the handling of arrays, loops, and expressions; language features that are traditionally difficult to handle in test data generation systems. The paper presents the test data generation procedure, examples to explain the working of the procedure, and results from a proof-of-concept implementation. Copyright © 1999 John Wiley & Sons, Ltd.

Dynamic slicing algorithms have been considered to aid in debugging for many years. However, as far as we know, no detailed studies on evaluating the benefits of using dynamic slicing for detecting faulty statements in programs have been carried out. We have developed a dynamic slicing framework that uses dynamic instrumentation to efficiently collect dynamic slices and reduced ordered Binary Decision Diagrams (roBDDs) to compactly store them. We have used the above framework to implement three variants of dynamic slicing algorithms including: data slicing, full slicing, and relevant slicing algorithms. We have carried out detailed experiments to evaluate these algorithms. Our results show that full slices and relevant slices can considerably reduce the subset of program statements that need to be examined to locate faulty statements. We expect that the observations presented here will enable development of new slicing based algorithms for automated debugging.

Program slicing is a well-known program analysis technique that extracts the elements of a program related to a particular computation. Based on modular monadic semantics of a programming language, this paper presents a new formal method for slicing, called modular monadic slicing, by abstracting the computation of slicing as a slice monad transformer. With the use of slice transformer, the feature of program slicing can be combined in a modular way into semantic descriptions of the program analyzed. According to these, this paper gives both monadic dynamic and static slicing algorithms. They compute program slices directly on abstract syntax, without the needs to explicitly construct intermediate structures such as dependence graphs, or to record an execution history in dynamic slicing algorithm.

The complexity of IT systems and the criticality of robust IT systems is constantly increasing. Testing a system requires consideration of different protocols and interfaces, which makes testing hard and expensive. Test automation is required to improve the quality of systems without cost explosion. Many standards like HTML and FTP are semi–formally defined in RFCs, which makes a generic algorithm for test data generation based on RFC relevant. The proposed approach makes it possible to automatically generate test data for protocols defined as ABNF in RFCs for robustness tests. The introduced approach was shown in practice by generating SIP messages based on the RFC specification of SIP. This approach shows the possibility to generate data for any RFC that uses ABNF, and provides a solid foundation for further empirical evaluation and extension for software testing purposes.

Application of Negative Selection Algorithm (NSA) for test data generation of path testing

Structural test data generation using a memetic ant colony optimization based on evolution strategies

Traditional program slicing techniques are crucial for early bug detection and manual/automated debugging of online code snippets. Nevertheless, their inability to handle incomplete code hinders their real-world applicability in such scenarios. To overcome these challenges, we present NS-Slicer, a novel learning-based approach that predicts static program slices for both complete and partial code Our tool leverages a pre-trained language model to exploit its understanding of fine-grained variable-statement dependencies within source code. With this knowledge, given a variable at a specific location and a statement in a code snippet, NS-Slicer determines whether the statement belongs to the backward slice or forward slice, respectively. We conducted a series of experiments to evaluate NS-Slicer's performance. On complete code, it predicts the backward and forward slices with an F1-score of 97.41% and 95.82%, respectively, while achieving an overall F1-score of 96.77%. Notably, in 85.20% of the cases, the static program slices predicted by NS-Slicer exactly match entire slices from the oracle. For partial programs, it achieved an F1-score of 96.77%–97.49% for backward slicing, 92.14%–95.40% for forward slicing, and an overall F1-score of 94.66%–96.62%. Furthermore, we demonstrate NS-Slicer's utility in vulnerability detection (VD), integrating its predicted slices into an automated VD tool. In this setup, the tool detected vulnerabilities in Java code with a high F1-score of 73.38%. We also include the analyses studying NS-Slicer’s promising performance and limitations, providing insights into its understanding of intrinsic code properties such as variable aliasing, leading to better slicing.

One of the ways of test data generation is using the path-oriented (path-wise) test data generator. This generator takes the program code and test adequacy criteria as input and generates the test data in order to satisfy the adequacy criteria. One of the problems of this generator in test data generation is the lack of attention to generating the diagnostic test data. In this paper a new approach has been proposed for path-oriented test data generation and by utilizing it, test data is generated automatically realizing the goal of discovering more important errors in the least amount of time. Since that some of the instructions of any programming language are more error-prone, so the paths that contain these instructions are selected for perform test data generation process. Then, the input domains of these paths variables are divided by a divide-and-conquer algorithm to the subdomains. A set of different subdomains is called hyper cuboids, and test data will be generated by these hyper cuboids. We apply our method in some programs, and compare it with some previous methods. Experimental results show proposed approach outperforms same previous approaches.

Automatic test data generation from a software specification prepares test cases for software developers before their code development phase. Having test cases before coding, helps the developers to control their code to conform to the specification. In state-based specifications, paths from the initial state to the final state may be varied, this is called parallel paths. In order to generate test data which cover all behaviors in the specification, test data for each parallel path should be generated. This paper proposes an enhanced genetic algorithm(GA)-based approach to resolve the parallel paths from the UML state machine diagram. The proposed approach is improved from our previous study which uses a GA based test data generation method for only one path. The approach identifies parallel paths to cover all transitions. GA evolves a number of suitable test data sets - one test data set for each parallel path. The best test data for each parallel path are picked and used for calculating the overall coverage test data. The experimental results show improved coverage results with the enhanced approach for a number of case studies with parallel paths.

Program slicing techniques are widely used in the field of program debugging and testing, It is divided into static slicing and dynamic slicing. Existing dynamic slicing methods are inadequate, To solve these problems, A new dynamic slicing algorithm is designed, This algorithm based on coverage testing, The core idea is to establish a dynamic dependence graph, And the construction of this dependence graph is based on the coverage testing. This algorithm not only uses the static information, but also uses the dynamic execution information, The advantage of this algorithm is that it does not recalculate when the standard of slice changes.

Automatic Programming Assessment (APA) is one of the vital methods that has been applied around Computer Science education in realizing automated marking and grading on students’ programming exercises or assignments. APA is fundamentally relying upon a test data generation process to perform a dynamic testing. Recently in Software Testing (ST) research, it has been proven that the adoption of any Meta-Heuristic Search Techniques (MHSTs) is able to improve the efficiency of generating adequate and optimal test data. Unfortunately, current studies on APA have not yet usefully incorporated the techniques to include a better quality program testing coverage by considering the optimal size of generated test data. Thus, our study propose a method of generating and locating an adequate test data with optimal in size by adapting a MHST to satisfy the dynamic-functional testing in APA (or is called DyFunFPA-TDG method). In this paper, we merely focus on revealing the design of the method with a sample of the generated test cases to be used in APA. This method able to assist educators of elementary programming courses to provide the means of deriving and generating adequate test data with optimal in size regardless of having the expertise in specific knowledge of test cases design.KeywordsAutomatic programming assessmentTest data generationDynamic testingFlower pollination algorithm

In view of the shortcomings of the test data generation algorithm including particle swarm optimization algorithm and ant colony algorithm, a new algorithm is proposed, which is based on the combination of particle swarm algorithm and parameter adjustment. This algorithm can dynamically adjust its search capabilities based on the fitness value of particles , combine the advantages of particle swarm optimization (PSO) algorithm and ant colony algorithm（ACO）to ensure the convergence and accuracy of the algorithm. Experiments show that the new algorithm can effectively improve the efficiency of test data generation.

In software testing process, test data generation represents an important step for high quality software, even for mobile devices. As proposed in previous works, a potential source for random data generation is represented by the UI layout files that are used for almost all mobile platforms (Android, iOS, Windows Phone/Mobile). This paper continues the previous work and presents a test data generation system based on Android layout files. The test data generator uses DSL files as input and generates test data that conform to several testing principles. The generated test data could be stored in XML files or any format required by the testing frameworks.

This paper uses a projection theory of slicing to formalize the definition of executable dynamic and forward program slicing. Previous definitions, when given, have been operational, and previous descriptions have been algorithmic. The projection framework is used to provide a declarative formulation in terms of the different equivalences preserved by the different forms of slicing. The analysis of dynamic slicing reveals that the slicing criterion introduced by Korel and Laski contains three inter-woven criteria. It is shown how these three conceptually distinct criteria can be disentangled to reveal two new criteria. The analysis of dynamic slicing also reveals that the subsumes relationship between static and dynamic slicing is more intricate that previous authors have claimed. Finally, the paper uses the projection theory to investigate theoretical properties of forward slicing. This is achieved by first re-formulating forward slicing to provide an executable forward slice. This definition allows for formal investigation of the relationship between forward and backward slicing

Automated test data generation is a challenging problem for researchers in the area of software testing. Up until now, most of the work on test data generation is at unit level. Until level test data generation involves the execution of test path at unit level where interaction with other components is minimum. Test data generation for unit testing involves a single path and there is no usage of formal and actual parameters. The problem of automated test data generation becomes very challenging when we move to other levels of testing including integration testing and system level testing. At integration level, the variables are passed as arguments to other components and variables change their names; also multiple paths are executed from different components to ensure proper functionality. Recently evolutionary approaches have been proven a powerful tool for test data generation. In this paper, we have proposed a novel approach for test data generation for coupling based integration testing using particle swarm optimization. Up until now, there is no research for test data generation for coupling based integration testing using particle swarm optimization. Our approach takes the coupling path as input, containing different sub paths, and generates the test data using particle swarm optimization. We have also proposed architecture of tool for automation of our approach. In future, we will implement our proposed approach and will perform different experiments to prove its significance.