Detecting Trivial Mutant Equivalences via Compiler Optimisations

Mutation testing realises the idea of fault-based testing, i.e., using artificial defects to guide the testing process. It is used to evaluate the adequacy of test suites and to guide test case generation. It is a potentially powerful form of testing, but it is well-known that its effectiveness is inhibited by the presence of equivalent mutants. We recently studied Trivial Compiler Equivalence (TCE) as a simple, fast and readily applicable technique for identifying equivalent mutants for C programs. In the present work, we augment our findings with further results for the Java programming language. TCE can remove a large portion of all mutants because they are determined to be either equivalent or duplicates of other mutants. In particular, TCE equivalent mutants account for 7.4% and 5.7% of all C and Java mutants, while duplicated mutants account for a further 21% of all C mutants and 5.4% Java mutants, on average. With respect to a benchmark ground truth suite (of known equivalent mutants), approximately 30% (for C) and 54% (for Java) are TCE equivalent. It is unsurprising that results differ between languages, since mutation characteristics are language-dependent. In the case of Java, our new results suggest that TCE may be particularly effective, finding almost half of all equivalent mutants

Symmetry in labeled transition systems

Symmetry is defined for labeled transition systems, and it is shown how symmetrical systems can be symmetrically decomposed into components. The central question is under what conditions one such component may represent the whole system, in the sense that one symmetrical system is bisimilar to a second if and only if a component of the first is equivalent to a component of the second. The theory developed is illustrated by three case studies, i.e. the alternating bit protocol, Peterson's algorithm and the Dining Philosophers

Threats to the validity of mutation-based test assessment

Much research on software testing and test techniques relies on experimental studies based on mutation testing. In this paper we reveal that such studies are vulnerable to a potential threat to validity, leading to possible Type I errors; incorrectly rejecting the Null Hypothesis. Our findings indicate that Type I errors occur, for arbitrary experiments that fail to take countermeasures, approximately 62% of the time. Clearly, a Type I error would potentially compromise any scientific conclusion. We show that the problem derives from such studies’ combined use of both subsuming and subsumed mutants. We collected articles published in the last two years at three leading software engineering conferences. Of those that use mutation-based test assessment, we found that 68% are vulnerable to this threat to validity

Space-Aware Ambients and Processes

Resource control has attracted increasing interest in foundational research on distributed systems. This paper focuses on space control and develops an analysis of space usage in the context of an ambient-like calculus with bounded capacities and weighed processes, where migration and activation require space. A type system complements the dynamics of the calculus by providing static guarantees that the intended capacity bounds are preserved throughout the computation

Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects

Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of Edge Potential Functions (EPF) with a powerful matching tool based on Genetic Algorithms (GA). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works