An efficient distributed algorithm for computing minimal hitting sets

Computing minimal hitting sets for a collection of sets is an important problem in many domains (e.g., Spectrum-based Fault Localization). Being an NP-Hard problem, exhaustive algorithms are usually prohibitive for real-world, often large, problems. In practice, the usage of heuristic based approaches trade-off completeness for time efficiency. An example of such heuristic approaches is STACCATO, which was proposed in the context of reasoning-based fault localization. In this paper, we propose an efficient distributed algorithm, dubbed MHS2, that renders the sequential search algorithm STACCATO suitable to distributed, Map-Reduce environments. The results show that MHS2 scales to larger systems (when compared to STACCATO), while entailing either marginal or small run time overhead

Evolutionary computing driven search based software testing and correction

For a given program, testing, locating the errors identified, and correcting those errors is a critical, yet expensive process. The field of Search Based Software Engineering (SBSE) addresses these phases by formulating them as search problems. This dissertation addresses these challenging problems through the use of two complimentary evolutionary computing based systems. The first one is the Fitness Guided Fault Localization (FGFL) system, which novelly uses a specification based fitness function to perform fault localization. The second is the Coevolutionary Automated Software Correction (CASC) system, which employs a variety of evolutionary computing techniques to perform testing, correction, and verification of software. In support of the real world application of these systems, a practitioner\u27s guide to fitness function design is provided. For the FGFL system, experimental results are presented that demonstrate the applicability of fitness guided fault localization to automate this important phase of software correction in general, and the potential of the FGFL system in particular. For the fitness function design guide, the performance of a guide generated fitness function is compared to that of an expert designed fitness function demonstrating the competitiveness of the guide generated fitness function. For the CASC system, results are presented that demonstrate the system\u27s abilities on a series of problems of both increasing size as well as number of bugs present. The system presented solutions more than 90% of the time for versions of the programs containing one or two bugs. Additionally, scalability results are presented for the CASC system that indicate that success rate linearly decreases with problem size and that the estimated convergence rate scales at worst linearly with problem size --Abstract, page ii

Automatic Repair of Buggy If Conditions and Missing Preconditions with SMT

We present Nopol, an approach for automatically repairing buggy if conditions and missing preconditions. As input, it takes a program and a test suite which contains passing test cases modeling the expected behavior of the program and at least one failing test case embodying the bug to be repaired. It consists of collecting data from multiple instrumented test suite executions, transforming this data into a Satisfiability Modulo Theory (SMT) problem, and translating the SMT result -- if there exists one -- into a source code patch. Nopol repairs object oriented code and allows the patches to contain nullness checks as well as specific method calls.Comment: CSTVA'2014, India (2014

Improving Fault Localization for Simulink Models using Search-Based Testing and Prediction Models

One promising way to improve the accuracy of fault localization based on statistical debugging is to increase diversity among test cases in the underlying test suite. In many practical situations, adding test cases is not a cost-free option because test oracles are developed manually or running test cases is expensive. Hence, we require to have test suites that are both diverse and small to improve debugging. In this paper, we focus on improving fault localization of Simulink models by generating test cases. We identify three test objectives that aim to increase test suite diversity. We use these objectives in a search-based algorithm to generate diversified but small test suites. To further minimize test suite sizes, we develop a prediction model to stop test generation when adding test cases is unlikely to improve fault localization. We evaluate our approach using three industrial subjects. Our results show (1) the three selected test objectives are able to significantly improve the accuracy of fault localization for small test suite sizes, and (2) our prediction model is able to maintain almost the same fault localization accuracy while reducing the average number of newly generated test cases by more than half