On the limits of mutation analysis

Mutation analysis is the gold standard for evaluating test-suite adequacy. It involves exhaustive seeding of all small faults in a program and evaluating the effectiveness of test suites in detecting these faults. Mutation analysis subsumes numerous structural coverage criteria, approximates fault detection capability of test suites, and the faults produced by mutation have been shown to be similar to the real faults. This dissertation looks at the effectiveness of mutation analysis in terms of its ability to evaluate the quality of test suites, and how well the mutants generated emulate real faults. The effectiveness of mutation analysis hinges on its two fundamental hypotheses: The competent programmer hypothesis, and the coupling effect. The competent programmer hypothesis provides the model for the kinds of faults that mutation operators emulate, and the coupling effect provides guarantees on the ratio of faults prevented by a test suite that detects all simple faults to the complete set of possible faults. These foundational hypotheses determine the limits of mutation analysis in terms of the faults that can be prevented by a mutation adequate test suite. Hence, it is important to understand what factors affect these assumptions, what kinds of faults escape mutation analysis, and what impact interference between faults (coupling and masking) have. A secondary concern is the computational footprint of mutation analysis. Mutation analysis requires the evaluation of numerous mutants, each of which potentially requires complete test runs to evaluate. Numerous heuristic methods exist to reduce the number of mutants that need to be evaluated. However, we do not know the effect of these heuristics on the quality of mutants thus selected. Similarly, whether the possible improvement in representation using these heuristics are subject to any limits have also not been studied in detail. Our research investigates these fundamental questions in mutation analysis both empirically and theoretically. We show that while a majority of faults are indeed small, and hence within a finite neighborhood of the correct version, their size is larger than typical mutation operators. We show that strong interactions between simple faults can produce complex faults that are semantically unrelated to the component faults, and hence escape first order mutation analysis. We further validate the coupling effect for a large number of real-world faults, provide theoretical support for fault coupling, and evaluate its theoretical and empirical limits. Finally, we investigate the limits of heuristic mutation reduction strategies in comparison with random sampling in representativeness and find that they provide at most limited improvement. These investigations underscore the importance of research into new mutation operators and show that the potential benefit far outweighs the perceived drawbacks in terms of computational cost.Keywords: fault interaction, mutation analysis, software engineering, software testing, theoretical analysis, software faults, empirical analysi

Knowledge derivation and data mining strategies for probabilistic functional integrated networks

PhDOne of the fundamental goals of systems biology is the experimental verification of the interactome: the entire complement of molecular interactions occurring in the cell. Vast amounts of high-throughput data have been produced to aid this effort. However these data are incomplete and contain high levels of both false positives and false negatives. In order to combat these limitations in data quality, computational techniques have been developed to evaluate the datasets and integrate them in a systematic fashion using graph theory. The result is an integrated network which can be analysed using a variety of network analysis techniques to draw new inferences about biological questions and to guide laboratory experiments. Individual research groups are interested in specific biological problems and, consequently, network analyses are normally performed with regard to a specific question. However, the majority of existing data integration techniques are global and do not focus on specific areas of biology. Currently this issue is addressed by using known annotation data (such as that from the Gene Ontology) to produce process-specific subnetworks. However, this approach discards useful information and is of limited use in poorly annotated areas of the interactome. Therefore, there is a need for network integration techniques that produce process-specific networks without loss of data. The work described here addresses this requirement by extending one of the most powerful integration techniques, probabilistic functional integrated networks (PFINs), to incorporate a concept of biological relevance. Initially, the available functional data for the baker’s yeast Saccharomyces cerevisiae was evaluated to identify areas of bias and specificity which could be exploited during network integration. This information was used to develop an integration technique which emphasises interactions relevant to specific biological questions, using yeast ageing as an exemplar. The integration method improves performance during network-based protein functional prediction in relation to this process. Further, the process-relevant networks complement classical network integration techniques and significantly improve network analysis in a wide range of biological processes. The method developed has been used to produce novel predictions for 505 Gene Ontology biological processes. Of these predictions 41,610 are consistent with existing computational annotations, and 906 are consistent with known expert-curated annotations. The approach significantly reduces the hypothesis space for experimental validation of genes hypothesised to be involved in the oxidative stress response. Therefore, incorporation of biological relevance into network integration can significantly improve network analysis with regard to individual biological questions

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

UTPA Undergraduate Catalog 2011-2013

https://scholarworks.utrgv.edu/edinburglegacycatalogs/1076/thumbnail.jp