Software Test Case Generation Research Articles

Topological Data Analysis for Software Test Cases Generation

The escalating development of digital technologies over the last several decades has given rise to an accompanying surge in data analysis.The software based smart applications increase the importance of behaviour analysis. Software testing of expert systems such as electronic health records, health information system and software as a medical device (SaMD) refers to detect the difference between expected behaviour and actual outcome during healthcare expert systems development. Test cases are the core source of effective software testing. Test cases generation for expert systems are discover challenges of identifying the expected behaviour of the system, where the decision logic is obtained via a data –driven paradigm. In the traditional system software object oriented expected behaviour, provide the clear test cases does not change the flow such as in the healthcare system. Intelligent software test case generation approach required for smart health systems. In this research contributes key performance indicators from massive data sets, node –link diagrams from decision trees, test cases from adjacency matrix are elaborated. The experimental results of healthcare expert systems provide empirically evidence that topological data analysis are compact contribution for software requirement validation.

Software Test Case Generation Tools and Techniques: A Review

Software Industry is evolving at a very fast pace since last two decades. Many software developments, testing and test case generation approaches have evolved in last two decades to deliver quality products and services. Testing plays a vital role to ensure the quality and reliability of software products. In this paper authors attempted to conduct a systematic study of testing tools and techniques. Six most popular e-resources called IEEE, Springer, Association for Computing Machinery (ACM), Elsevier, Wiley and Google Scholar to download 738 manuscripts out of which 125 were selected to conduct the study. Out of 125 manuscripts selected, a good number approx. 79% are from reputed journals and around 21% are from good conference of repute. Testing tools discussed in this paper have broadly been divided into five different categories: open source, academic and research, commercial, academic and open source, and commercial & open source. The paper also discusses several benchmarked datasets viz. Evosuite 10, SF100 Corpus, Defects4J repository, Neo4j, JSON, Mocha JS, and Node JS to name a few. Aim of this paper is to make the researchers aware of the various test case generation tools and techniques introduced in the last 11 years with their salient features.

Software Test Path Identification for Virtual Assistants Based on Natural Language Conversation Flow Chart

The growth and pervasiveness of the mobile phone usage provides opportunities for new types of communications. The technology is mainly used in the communications between human and electronic assistant is chatbot. A chatbot is a simulated data based that enabled human conversation. The electronic assistant is currently used for a variety of purposes. The chatbot database flow is the important activity for the development of electronic assistants. The process of chatbot testing is based on the well-formalized test paths. The test paths are based on the chatbot trace in the database. Trace path identification during the development of the chatbot software is the challenging process. This paper presents the methodology to identify the test path for electronic assistants using natural language conversation flow chart. Chatbot database flow is one of the few specification languages supporting for formal description into an applied specification. The database natural language conversation flow chart divided into several trace using the proposed algorithm. Finally, the chatbot intent trace has provided the path for software test case generation. In the experiments shows testing based on the generated test paths has yielded the effective coverage criteria in the chatbot software development.

A Systematic Review of Agent-Based Test Case Generation for Regression Testing

There is an urgent need to create awareness about the potential benefits of using agents in software test case generation and to identify the need to develop agent-based regression testing techniques and approaches. It may help in reducing time and cost required for testing. This study reports systematic literature review of existing test case generation approaches for regression testing and agent-based software testing systems. The emphasis is articulated on agent-based regression test case generation. Further research directions are recommended. In the systematic literature review, we framed three sets of research questions. Based on our inclusion and exclusion criteria, we identified 115 potential research papers on test case generation in regression testing and agent-based software testing. We explored journals, international conferences, workshops and identified 59 studies in test case generation for regression testing and 56 studies in agent-based software testing. The data extracted from our study are classified into seven broader areas of agent-based software testing. Based on our systematic literature survey, we recognized available techniques, approaches, platforms as well as methodologies for regression test case generation and developing agent-based software testing systems. This study will benefit the researchers to carry forward their work in the domain of regression test case generation and agent-based software testing. To cut down on schedule and cost, mobile agent-based software testing can be a promising alternative.

The Application of Meta-Heuristic Algorithms in Automatic Software Test Case Generation

Nowadays, software test is one of the most important activities that software’s quality will be certified by it. Test operation includes program’s implement on test case set and comparison of results with expected one. Manual test case for operation test program and error detect is time consuming with insufficient precision and complicated coverage of program, so, the use of algorithms in automatic test case generation has been considered. Meta-heuristic algorithms are known tools which are optimized and used in test case generation. Most of complicated matters need a lot of possible states assessment in order to reach the valid answer. With the proper answer, test case optimization and meta-heuristic algorithms play a constructive role. In this paper we would compare methods and their traits, and the software test case generation methods based on metaheuristic algorithms with their description.

An orchestrated survey of methodologies for automated software test case generation

Test case generation is among the most labour-intensive tasks in software testing. It also has a strong impact on the effectiveness and efficiency of software testing. For these reasons, it has been one of the most active research topics in software testing for several decades, resulting in many different approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is contributed by world-renowned active researchers on the technique, and briefly covers the basic ideas underlying the method, the current state of the art, a discussion of the open research problems, and a perspective of the future development of the approach. As a whole, the paper aims at giving an introductory, up-to-date and (relatively) short overview of research in automatic test case generation, while ensuring a comprehensive and authoritative treatment.

Radial basis function neural network based approach to test oracle

Software testing is an important discipline, and consumes significant amount of effort. A proper strategy is required to design and generate test cases systematically and effectively. In this paper automated software test case generation with Radial Basis Function Neural Network (RBFNN) has been proposed and empirically validated with the help of a case study and compared with other techniques of soft computing. Experimental results show that RBFNN is one of the best technique for automated test case generation.

Automated test data generation using a scatter search approach

The techniques for the automatic generation of test cases try to efficiently find a small set of cases that allow a given adequacy criterion to be fulfilled, thus contributing to a reduction in the cost of software testing. In this paper we present and analyze two versions of an approach based on the scatter search metaheuristic technique for the automatic generation of software test cases using a branch coverage adequacy criterion. The first test case generator, called TCSS, uses a diversity property to extend the search of test cases to all branches of the program under test in order to generate test cases that cover these. The second, called TCSS-LS, is an extension of the previous test case generator which combines the diversity property with a local search method that allows the intensification of the search for test cases that cover the difficult branches. We present the results obtained by our generators and carry out a detailed comparison with many other generators, showing a good performance of our approach.