Mutation testing of a test suite and a program provides a way to measure the quality of the test suite. In essence, mutation testing is a form of sensitivity testing: by running mutated versions of the program against the test suite, mutation testing measures the suite's sensitivity for detecting bugs that a programmer might introduce into the program. This paper introduces a technique to improve mutation testing that we call wild-caught mutants; it provides a method for creating potential faults that are more closely coupled with changes made by actual programmers. This technique allows the mutation tester to have more certainty that the test suite is sensitive to the kind of changes that have been observed to have been made by programmers in real-world cases. CCS CONCEPTS • Software and its engineering → Software configuration management and version control systems; Software testing and debugging; Parsers;

Program coverage is used across many stages of software development. While common during testing, program coverage has also found use outside the test lab, in production software. However, production software has stricter requirements on run-time overheads, and may limit possible program instrumentation. Thus, optimizing the placement of probes to gather program coverage is important. We introduce and study the problem of customized program coverage optimization. We generalize previous work that optimizes for complete coverage instrumentation with a system that adapts optimization to customizable program coverage requirements. Specifically, our system allows a user to specify desired coverage locations and to limit legal instrumentation locations. We prove that the problem of determining optimal coverage probes is NP-hard, and we present a solution based on mixed integer linear programming. Due to the computational complexity of the problem, we also provide two practical approximation approaches. We evaluate the effectiveness of our approximations across a diverse set of benchmarks, and show that our techniques can substantially reduce instrumentation while allowing the user immense freedom in defining coverage requirements. When naïve instrumentation is dense or expensive, our optimizations succeed in lowering execution time overheads. CCS Concepts •Software and its engineering → Software testing and debugging; Software post-development issues; Software performance; •Mathematics of computing → Integer programming;

No abstract

No abstract

Abstract. [Context and motivation]Requirements form the foundation of software systems. The quality of the requirements influences the quality of the developed software. [Question/problem] One of the main requirement issues is inconsistency, particularly onerous when the requirements concern temporal constraints. Manual checking whether temporal requirements are consistent is tedious and error prone and may be prohibitively expensive when the number of requirements is large. [Principal ideas/results] We show that answer-set programming tools (ASP) can be successfully applied to detect inconsistencies in software and system requirements. Our assumption is that these requirements are given in a formal requirement specification language called Temporal Action Language (TeAL).[Contribution] We present a translation from TeAL to the ASP language format accepted by clingcon. We show that clingcon can analyze requirements for several real software systems, verifying their consistency or identifying inconsistencies. We also examine the performance of the clingcon translation.