Mapping the Structure and Evolution of Software Testing Research Over the Past Three Decades

Background: The field of software testing is growing and rapidly-evolving. Aims: Based on keywords assigned to publications, we seek to identify predominant research topics and understand how they are connected and have evolved. Method: We apply co-word analysis to map the topology of testing research as a network where author-assigned keywords are connected by edges indicating co-occurrence in publications. Keywords are clustered based on edge density and frequency of connection. We examine the most popular keywords, summarize clusters into high-level research topics, examine how topics connect, and examine how the field is changing. Results: Testing research can be divided into 16 high-level topics and 18 subtopics. Creation guidance, automated test generation, evolution and maintenance, and test oracles have particularly strong connections to other topics, highlighting their multidisciplinary nature. Emerging keywords relate to web and mobile apps, machine learning, energy consumption, automated program repair and test generation, while emerging connections have formed between web apps, test oracles, and machine learning with many topics. Random and requirements-based testing show potential decline. Conclusions: Our observations, advice, and map data offer a deeper understanding of the field and inspiration regarding challenges and connections to explore.Comment: To appear, Journal of Systems and Softwar

How Closely are Common Mutation Operators Coupled to Real Faults?

In mutation testing, faulty versions of a program are generated through automated modifications of source code. These mutants are used to assess and improve test suite quality, under the assumption that detection of mutants is indicative of a test suite\u27s ability to detect real faults - i.e., that mutants and faults have a semantic relationship. Improving the effectiveness - in both cost and quality - of mutation testing may lie in better understanding this relationship, in particular with regard to how individual mutation operators (types) couple to real faults.In this study, we examine coupling between 32,002 mutants produced by 31 mutation operators and 144 real faults, using a scale based on number of failing tests and reasons for failure. Ultimately, we observed that 9.92% of the mutants are strongly coupled to real faults, and 51.03% of the faults have at least one strongly coupled mutant. We identify and examine mutation operators with the highest median coupling, as well as the operators that tend to produce non-compiling mutants, undetected mutants, and mutants that cause tests other than those that detect the actual fault to fail. We also examine how coupling could be used to filter the set of operators employed, leading to potentially significant cost savings during mutation testing. Our findings could lead to improvements in how mutation testing is applied, improved implementation of specific mutation operators, and inspiration for new mutation operators