A Survey on Software Testing Techniques using Genetic Algorithm

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.Comment: 13 Page

Evolutionary Computing based an Efficient and Cost Effective Software Defect Prediction System

The earlier defect prediction and fault removal can play a vital role in ensuring software reliability and quality of service In this paper Hybrid Evolutionary computing based Neural Network HENN based software defect prediction model has been developed For HENN an adaptive genetic algorithm A-GA has been developed that alleviates the key existing limitations like local minima and convergence Furthermore the implementation of A-GA enables adaptive crossover and mutation probability selection that strengthens computational efficiency of our proposed system The proposed HENN algorithm has been used for adaptive weight estimation and learning optimization in ANN for defect prediction In addition a novel defect prediction and fault removal cost estimation model has been derived to evaluate the cost effectiveness of the proposed system The simulation results obtained for PROMISE and NASA MDP datasets exhibit the proposed model outperforms Levenberg Marquardt based ANN system LM-ANN and other systems as well And also cost analysis exhibits that the proposed HENN model is approximate 21 66 cost effective as compared to LM-AN

A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation

Otsingupõhine tarkvara testimine kasutab metaheuristilisi algoritme, et automatiseerida testide genereerimist. Selle töö eesmärgiks on osaliselt taasluua 2010. aastal kirjutatud Ali et al. artikkel, et uurida, kuidas on aastatel 2008-2015 kasutatud metaheuristilisi algoritme testide loomiseks. See töö analüüsib, kuidas on antud artiklid koostatud ning kuidas neis on algoritmide maksumust ja efektiivsust hinnatud. Kogutud tulemusi võrreldakse Ali et al. tulemustega.Search based software testing uses metaheuristic algorithms to automate the generation of test cases. This thesis partially replicates a literature study published in 2010 by Ali et al. to determine how studies published in 2008-2015 use metaheuristic algorithms to automate the generation of test cases. The thesis analyses how these studies were conducted and how the cost-effectiveness is assessed in these papers. The trends detected in the new publications are compared to those presented in Ali et al

A Hybrid Test Optimization Framework - Coupling Genetic Algorithm with Local Search Technique

Quality of test cases is determined by their ability to uncover as many errors as possible in the software code. In our approach, we applied Hybrid Genetic Algorithm (HGA) for improving the quality of test cases. This improvement can be achieved by analyzing both mutation score and path coverage of each test case. Our approach selects effective test cases that have higher mutation score and path coverage from a near infinite number of test cases. Hence, the final test set size is reduced which in turn reduces the total time needed in testing activity. In our proposed framework, we included two improvement heuristics, namely RemoveTop and LocalBest, to achieve near global optimal solution. Finally, we compared the efficiency of the test cases generated by our approach against the existing test case optimization approaches such as Simple Genetic Algorithm (SGA) and Bacteriologic Algorithm (BA) and concluded that our approach generates better quality test cases

eCrash: a framework for performing evolutionary testing on third-party Java components

The focus of this paper is on presenting a tool for generating test data by employing evolutionary search techniques, with basis on the information provided by the structural analysis and interpretation of the Java bytecode of third-party Java components, and on the dynamic execution of the instrumented test object. The main objective of this approach is that of evolving a set of test cases that yields full structural code coverage of the test object. Such a test set can be used for effectively performing the testing activity, providing confidence in the quality and robustness of the test object. The rationale of working at the bytecode level is that even when the source code is unavailable structural testing requirements can still be derived, and used to assess the quality of a test set and to guide the evolutionary search towards reaching specific test goals

International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome

Using dynamic analysis of Java bytecode for evolutionary object-oriented unit testing

The focus of this paper is on presenting a methodology for generating and optimizing test data by employing evolutionary search techniques, with basis on the information provided by the analysis and interpretation of Java bytecode and on the dynamic execution of the instrumented test object. The main reason to work at the bytecode level is that even when the source code is unavailable, structural testing requirements can still be derived and used to assess the quality of a given test set and to guide the evolutionary search towards reaching specific test goals. Java bytecode retains enough high-level information about the original source code for an underlying model for program representation to be built. The observations required to select or generate test data are obtained by employing dynamic analysis techniques – i.e. by instrumenting, tracing and analysing Java bytecode