Test Case Purification for Improving Fault Localization

Finding and fixing bugs are time-consuming activities in software development. Spectrum-based fault localization aims to identify the faulty position in source code based on the execution trace of test cases. Failing test cases and their assertions form test oracles for the failing behavior of the system under analysis. In this paper, we propose a novel concept of spectrum driven test case purification for improving fault localization. The goal of test case purification is to separate existing test cases into small fractions (called purified test cases) and to enhance the test oracles to further localize faults. Combining with an original fault localization technique (e.g., Tarantula), test case purification results in better ranking the program statements. Our experiments on 1800 faults in six open-source Java programs show that test case purification can effectively improve existing fault localization techniques

Spectrum-Based Fault Localization in Model Transformations

Model transformations play a cornerstone role in Model-Driven Engineering (MDE), as they provide the essential mechanisms for manipulating and transforming models. The correctness of software built using MDE techniques greatly relies on the correctness of model transformations. However, it is challenging and error prone to debug them, and the situation gets more critical as the size and complexity of model transformations grow, where manual debugging is no longer possible. Spectrum-Based Fault Localization (SBFL) uses the results of test cases and their corresponding code coverage information to estimate the likelihood of each program component (e.g., statements) of being faulty. In this article we present an approach to apply SBFL for locating the faulty rules in model transformations. We evaluate the feasibility and accuracy of the approach by comparing the effectiveness of 18 different stateof- the-art SBFL techniques at locating faults in model transformations. Evaluation results revealed that the best techniques, namely Kulcynski2, Mountford, Ochiai, and Zoltar, lead the debugger to inspect a maximum of three rules to locate the bug in around 74% of the cases. Furthermore, we compare our approach with a static approach for fault localization in model transformations, observing a clear superiority of the proposed SBFL-based method.Comisión Interministerial de Ciencia y Tecnología TIN2015-70560-RJunta de Andalucía P12-TIC-186

Assessment and improvement of automated program repair mechanisms and components

2015 Spring.Includes bibliographical references.Automated program repair (APR) refers to techniques that locate and fix software faults automatically. An APR technique locates potentially faulty locations, then it searches the space of possible changes to select a program modification operator (PMO). The selected PMO is applied to a potentially faulty location thereby creating a new version of the faulty program, called a variant. The variant is validated by executing it against a set of test cases, called repair tests, which is used to identify a repair. When all of the repair tests are successful, the variant is considered a potential repair. Potential repairs that have passed a set of regression tests in addition to those included in the repair tests are deemed to be validated repairs. Different mechanisms and components can be applied to repair faults. APR mechanisms and components have a major impact on APR effectiveness, repair quality, and performance. APR effectiveness is the ability to and potential repairs. Repair quality is defined in terms of repair correctness and maintainability, where repair correctness indicates how well a potential repaired program retains required functionality, and repair maintainability indicates how easy it is to understand and maintain the generated potential repair. APR performance is the time and steps required to find a potential repair. Existing APR techniques can successfully fix faults, but the changes inserted to fix faults can have negative consequences on the quality of potential repairs. When a potential repair is executed against tests that were not included in the repair tests, the "repair" can fail. Such failures indicate that the generated repair is not a validated repair due to the introduction of other faults or the generated potential repair does not actually fix the real fault. In addition, some existing techniques add extraneous changes to the code that obfuscate the program logic and thus reduce its maintainability. APR effectiveness and performance can be dramatically degraded when an APR technique applies many PMOs, uses a large number of repair tests, locates many statements as potentially faulty locations, or applies a random search algorithm. This dissertation develops improved APR techniques and tool set to help optimize APR effectiveness, the quality of generated potential repairs, and APR performance based on a comprehensive evaluation of APR mechanisms and components. The evaluation involves the following: (1) the PMOs used to produce repairs, (2) the properties of repair tests used in the APR, (3) the fault localization techniques employed to identify potentially faulty statements, and (4) the search algorithms involved in the repair process. We also propose a set of guided search algorithms that guide the APR technique to select PMO that fix faults, which thereby improve APR effectiveness, repair quality, and performance. We performed a set of evaluations to investigate potential improvements in APR effectiveness, repair quality, and performance. APR effectiveness of different program modification operators is measured by the percent of fixed faults and the success rate. Success rate is the percentage of trials that result in potential repairs. One trial is equivalent to one execution of the search algorithm. APR effectiveness of different fault localization techniques is measured by the ability of a technique to identify actual faulty statements, and APR effectiveness of various repair test suites and search algorithms is also measured by the success rate. Repair correctness is measured by the percent of failed potential repairs for 100 trials for a faulty program, and the average percent of failed regression tests for N potential repairs for a faulty program; N is the number of potential repairs generated for 100 trials. Repair maintainability is measured by the average size of a potential repair, and the distribution of modifications throughout a potential repaired program. APR performance is measured by the average number of generated variants and the average total time required to find potential repairs. We built an evaluation framework creating a configurable mutation-based APR (MUT-APR) tool. MUT-APR allows us to vary the APR mechanisms and components. Our key findings are the following: (1) simple PMOs successfully fix faulty expression operators and improve the quality of potential repairs compared to other APR techniques that use existing code to repair faults, (2) branch coverage repair test suites improve APR effectiveness and repair quality significantly compared to repair test suites that satisfy statement coverage or random testing; however, they lowered APR performance, (3) small branch coverage repair test suites improved APR effectiveness, repair quality, and performance significantly compared to large branch coverage repair tests, (4) the Ochiai fault localization technique always identifies seeded faulty statements with an acceptable performance, and (5) guided random search algorithm improves APR effectiveness, repair quality, and performance compared to all other search algorithms; however, the exhaustive search algorithms is guaranteed a potential repair that failed fewer regression tests with a significant performance degradation as the program size increases. These improvements are incorporated into the MUT-APR tool for use in program repairs

A dynamic fault localization technique with noise reduction for java programs

Existing fault localization techniques combine various program features and similarity coefficients with the aim of precisely assessing the similarities among the dynamic spectra of these program features to predict the locations of faults. Many such techniques estimate the probability of a particular program feature causing the observed failures. They ignore the noise introduced by the other features on the same set of executions that may lead to the observed failures. In this paper, we propose both the use of chains of key basic blocks as program features and an innovative similarity coefficient that has noise reduction effect. We have implemented our proposal in a technique known as MKBC. We have empirically evaluated MKBC using three real-life medium-sized programs with real faults. The results show that MKBC outperforms Tarantula, Jaccard, SBI, and Ochiai significantly. © 2011 IEEE.published_or_final_versionThe 11th International Conference on Quality Software (QSIC 2011), Madrid, Spain, 13-14 July 2011. In International Conference on Quality Software Proceedings, 2011, p. 11-2

Semi-automatic fault localization

One of the most expensive and time-consuming components of the debugging process is locating the errors or faults. To locate faults, developers must identify statements involved in failures and select suspicious statements that might contain faults. In practice, this localization is done by developers in a tedious and manual way, using only a single execution, targeting only one fault, and having a limited perspective into a large search space. The thesis of this research is that fault localization can be partially automated with the use of commonly available dynamic information gathered from test-case executions in a way that is effective, efficient, tolerant of test cases that pass but also execute the fault, and scalable to large programs that potentially contain multiple faults. The overall goal of this research is to develop effective and efficient fault localization techniques that scale to programs of large size and with multiple faults. There are three principle steps performed to reach this goal: (1) Develop practical techniques for locating suspicious regions in a program; (2) Develop techniques to partition test suites into smaller, specialized test suites to target specific faults; and (3) Evaluate the usefulness and cost of these techniques. In this dissertation, the difficulties and limitations of previous work in the area of fault-localization are explored. A technique, called Tarantula, is presented that addresses these difficulties. Empirical evaluation of the Tarantula technique shows that it is efficient and effective for many faults. The evaluation also demonstrates that the Tarantula technique can loose effectiveness as the number of faults increases. To address the loss of effectiveness for programs with multiple faults, supporting techniques have been developed and are presented. The empirical evaluation of these supporting techniques demonstrates that they can enable effective fault localization in the presence of multiple faults. A new mode of debugging, called parallel debugging, is developed and empirical evidence demonstrates that it can provide a savings in terms of both total expense and time to delivery. A prototype visualization is provided to display the fault-localization results as well as to provide a method to interact and explore those results. Finally, a study on the effects of the composition of test suites on fault-localization is presented.Ph.D.Committee Chair: Harrold, Mary Jean; Committee Member: Orso, Alessandro; Committee Member: Pande, Santosh; Committee Member: Reiss, Steven; Committee Member: Rugaber, Spence