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

Faster Mutation Analysis via Equivalence Modulo States

Mutation analysis has many applications, such as asserting the quality of test suites and localizing faults. One important bottleneck of mutation analysis is scalability. The latest work explores the possibility of reducing the redundant execution via split-stream execution. However, split-stream execution is only able to remove redundant execution before the first mutated statement. In this paper we try to also reduce some of the redundant execution after the execution of the first mutated statement. We observe that, although many mutated statements are not equivalent, the execution result of those mutated statements may still be equivalent to the result of the original statement. In other words, the statements are equivalent modulo the current state. In this paper we propose a fast mutation analysis approach, AccMut. AccMut automatically detects the equivalence modulo states among a statement and its mutations, then groups the statements into equivalence classes modulo states, and uses only one process to represent each class. In this way, we can significantly reduce the number of split processes. Our experiments show that our approach can further accelerate mutation analysis on top of split-stream execution with a speedup of 2.56x on average.Comment: Submitted to conferenc

Unifying regression testing with mutation testing

textSoftware testing is the most commonly used methodology for validating quality of software systems. Conceptually, testing is simple, but in practice, given the huge (practically infinite) space of inputs to test against, it requires solving a number of challenging problems, including evaluating and reusing tests efficiently and effectively as software evolves. While software testing research has seen much progress in recent years, many crucial bugs still evade state-of-the-art approaches and cause significant monetary losses and sometimes are responsible for loss of life. My thesis is that a unified, bi-dimensional, change-driven methodology can form the basis of novel techniques and tools that can make testing significantly more effective and efficient, and allow us to find more bugs at a reduced cost. We propose a novel unification of the following two dimensions of change: (1) real manual changes made by programmers, e.g., as commonly used to support more effective and efficient regression testing techniques; and (2) mechanically introduced changes to code or specifications, e.g., as originally conceived in mutation testing for evaluating quality of test suites. We believe such unification can lay the foundation of a scalable and highly effective methodology for testing and maintaining real software systems. The primary contribution of my thesis is two-fold. One, it introduces new techniques to address central problems in both regression testing (e.g., test prioritization) and mutation testing (e.g., selective mutation testing). Two, it introduces a new methodology that uses the foundations of regression testing to speed up mutation testing, and also uses the foundations of mutation testing to help with the fault localization problem raised in regression testing. The central ideas are embodied in a suite of prototype tools. Rigorous experimental evaluation is used to validate the efficacy of the proposed techniques using a variety of real-world Java programs.Electrical and Computer Engineerin

A review paper: optimal test cases for regression testing using artificial intelligent techniques

The goal of the testing process is to find errors and defects in the software being developed so that they can be fixed and corrected before they are delivered to the customer. Regression testing is an essential quality testing technique during the maintenance phase of the program as it is performed to ensure the integrity of the program after modifications have been made. With the development of the software, the test suite becomes too large to be fully implemented within the given test cost in terms of budget and time. Therefore, the cost of regression testing using different techniques should be reduced, here we dealt many methods such as retest all technique, regression test selection technique (RTS) and test case prioritization technique (TCP). The efficiency of these techniques is evaluated through the use of many metrics such as average percentage of fault detected (APFD), average percentage block coverage (APBC) and average percentage decision coverage (APDC). In this paper we dealt with these different techniques used in test case selection and test case prioritization and the metrics used to evaluate their efficiency by using different techniques of artificial intelligent and describe the best of all

A Chemical Reaction Optimization Approach to Prioritize the Regression Test Cases of Object-Oriented Programs

Regression test case prioritization is used to improve certain performance goals. Limited resources force to choose an effective prioritization technique, which makes an ordering of the test cases so that the most suitable test case will be executed first. Executing regression test cases for a fixed time is all about time aware test case prioritization. Regression test case prioritization using chemical reaction optimization (CRO) for object-oriented programs is proposed in this paper. The effectiveness of the test case ordering was measured using average percentage of faults detected (APFD). Experiments on three object-oriented subject programs involving three different techniques were performed to judge the proposed approach. The empirical results indicate that the algorithm implemented using CRO gives a higher APFD value than the other two techniques

Selecting fault revealing mutants

Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem of selecting the fault revealing mutants, i.e., the mutants that are killable and lead to test cases that uncover unknown program faults. We formulate two variants of this problem: the fault revealing mutant selection and the fault revealing mutant prioritization. We argue and show that these problems can be tackled through a set of ‘static’ program features and propose a machine learning approach, named FaRM, that learns to select and rank killable and fault revealing mutants. Experimental results involving 1,692 real faults show the practical benefits of our approach in both examined problems. Our results show that FaRM achieves a good trade-off between application cost and effectiveness (measured in terms of faults revealed). We also show that FaRM outperforms all the existing mutant selection methods, i.e., the random mutant sampling, the selective mutation and defect prediction (mutating the code areas pointed by defect prediction). In particular, our results show that with respect to mutant selection, our approach reveals 23% to 34% more faults than any of the baseline methods, while, with respect to mutant prioritization, it achieves higher average percentage of revealed faults with a median difference between 4% and 9% (from the random mutant orderings)

Selecting fault revealing mutants

Mutant selection refers to the problem of choosing, among a large number of mutants, the (few) ones that should be used by the testers. In view of this, we investigate the problem of selecting the fault revealing mutants, i.e., the mutants that are killable and lead to test cases that uncover unknown program faults. We formulate two variants of this problem: the fault revealing mutant selection and the fault revealing mutant prioritization. We argue and show that these problems can be tackled through a set of ‘static’ program features and propose a machine learning approach, named FaRM, that learns to select and rank killable and fault revealing mutants. Experimental results involving 1,692 real faults show the practical benefits of our approach in both examined problems. Our results show that FaRM achieves a good trade-off between application cost and effectiveness (measured in terms of faults revealed). We also show that FaRM outperforms all the existing mutant selection methods, i.e., the random mutant sampling, the selective mutation and defect prediction (mutating the code areas pointed by defect prediction). In particular, our results show that with respect to mutant selection, our approach reveals 23% to 34% more faults than any of the baseline methods, while, with respect to mutant prioritization, it achieves higher average percentage of revealed faults with a median difference between 4% and 9% (from the random mutant orderings)