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Software testing is undertaken to ensure that the software meets the expected requirements. The intention is to find bugs, errors, or defects in the developed software so that they can be fixed before deployment. Testing of the software is needed even after it is deployed. Regression testing is an inevitable part of software development, and must be accomplished in the maintenance phase of software development to ensure software reliability. The existing literature presents a large amount of relevant knowledge about the types of techniques and approaches used in regression test case selection and prioritization (TCS&P), comparisons of techniques used in TCS&P, and the data used. Numerous secondary studies (surveys or reviews) have been conducted in the area of TCS&P. This study aimed to provide a comprehensive examination of the analysis of the enhancements in TCS&P using a thorough systematic literature review (SLR) of the existing secondary studies. This SLR provides: (1) a collection of all the valuable secondary studies (and their qualitative analysis); (2) a thorough analysis of the publications and the trends of the secondary studies; (3) a classification of the various approaches used in the secondary studies; (4) insight into the specializations and range of years covered in the secondary texts; (5) a comprehensive list of statistical tests and tools used in the area; (6) insight into the quality of the secondary studies based on the seven selected Research Paper Quality parameters; (7) the common problems and challenges encountered by researchers; (8) common gaps and limitations of the studies; and (9) the probable prospects for research in the field of TCS&P.

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Background: The paper presents a comparative evaluation of two techniques for test selection. Objective: The objective is to empirically evaluate the performance of a greedy approach and a search based approach for test case selection. Method: We conducted an experiment on 24 programs in different languages. Result: The time bound greedy approach yielded best result for 16 out of the 24 programs. The ACO approach could find the best result 100% times for 11 programs and at least 30-95% times for rest 13 programs by running ACO 10 times on each program. Yet the percentage reductions achieved in the size and execution time of the resultant test suite were almost similar in both the techniques. Conclusion: The results inspire the further use of both the techniques in regression testing.

This chapter builds the foundation of software testing techniques by classifying the various testing approaches and testing coverage criteria. It gradually advances in the concepts and process of Mutation Testing and its application areas. Mutation testing has been applied at both the source code level and specification level of the software under test. Mutation testing, when applied to the source code, is named as Program Mutation. Similarly, when applied to the specifications, it is named as Specification Mutation. The relevant Mutation Testing tools available for different programming languages for both program and Specification Mutations are hereby listed. Owing to the high cost incurred in applying Mutation Testing to industrial needs, the on-going endeavors of the researchers in the area are elaborated here. Applying nature-inspired algorithms along with Mutation Testing for data generation/selection/minimization is an upcoming area of research. Search based Mutation Testing (SBMT) applies evolutionary techniques like Genetic Algorithms or other metaheuristic approaches for automating the tasks associated with mutation testing, which otherwise requires a lot of human effort, thus, making it a practical approach. This chapter concludes by giving the seminal recent advancements in the area.

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