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

On practical adequate test suites for integrated test case prioritization and fault localization

An effective integration between testing and debugging should address how well testing and fault localization can work together productively. In this paper, we report an empirical study on the effectiveness of using adequate test suites for fault localization. We also investigate the integration of test case prioritization and statistical fault localization with a postmortem analysis approach. Our results on 16 test case prioritization techniques and four statistical fault localization techniques show that, although much advancement has been made in the last decade, test adequacy criteria are still insufficient in supporting effective fault localization. We also find that the use of branch-adequate test suites is more likely than statement-adequate test suites in the effective support of statistical fault localization. © 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. 21-3

A survey on test suite reduction frameworks and tools

Software testing is a widely accepted practice that ensures the quality of a System under Test (SUT). However, the gradual increase of the test suite size demands high portion of testing budget and time. Test Suite Reduction (TSR) is considered a potential approach to deal with the test suite size problem. Moreover, a complete automation support is highly recommended for software testing to adequately meet the challenges of a resource constrained testing environment. Several TSR frameworks and tools have been proposed to efficiently address the test-suite size problem. The main objective of the paper is to comprehensively review the state-of-the-art TSR frameworks to highlights their strengths and weaknesses. Furthermore, the paper focuses on devising a detailed thematic taxonomy to classify existing literature that helps in understanding the underlying issues and proof of concept. Moreover, the paper investigates critical aspects and related features of TSR frameworks and tools based on a set of defined parameters. We also rigorously elaborated various testing domains and approaches followed by the extant TSR frameworks. The results reveal that majority of TSR frameworks focused on randomized unit testing, and a considerable number of frameworks lacks in supporting multi-objective optimization problems. Moreover, there is no generalized framework, effective for testing applications developed in any programming domain. Conversely, Integer Linear Programming (ILP) based TSR frameworks provide an optimal solution for multi-objective optimization problems and improve execution time by running multiple ILP in parallel. The study concludes with new insights and provides an unbiased view of the state-of-the-art TSR frameworks. Finally, we present potential research issues for further investigation to anticipate efficient TSR frameworks

Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset

Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs. Each bug in Defects4J is provided with a test suite and at least one failing test case that triggers the bug. In this paper, we report on an experiment to explore the effectiveness of automatic repair on Defects4J. The result of our experiment shows that 47 bugs of the Defects4J dataset can be automatically repaired by state-of- the-art repair. This sets a baseline for future research on automatic repair for Java. We have manually analyzed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly fixed with test-suite based repair. This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial and incorrect patches still pass the test suite. With respect to practical applicability, it takes in average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totaling 17.6 days of computation time. All their systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair

Test case prioritization approaches in regression testing: A systematic literature review

Context Software quality can be assured by going through software testing process. However, software testing phase is an expensive process as it consumes a longer time. By scheduling test cases execution order through a prioritization approach, software testing efficiency can be improved especially during regression testing. Objective It is a notable step to be taken in constructing important software testing environment so that a system's commercial value can increase. The main idea of this review is to examine and classify the current test case prioritization approaches based on the articulated research questions. Method Set of search keywords with appropriate repositories were utilized to extract most important studies that fulfill all the criteria defined and classified under journal, conference paper, symposiums and workshops categories. 69 primary studies were nominated from the review strategy. Results There were 40 journal articles, 21 conference papers, three workshop articles, and five symposium articles collected from the primary studies. As for the result, it can be said that TCP approaches are still broadly open for improvements. Each approach in TCP has specified potential values, advantages, and limitation. Additionally, we found that variations in the starting point of TCP process among the approaches provide a different timeline and benefit to project manager to choose which approaches suite with the project schedule and available resources. Conclusion Test case prioritization has already been considerably discussed in the software testing domain. However, it is commonly learned that there are quite a number of existing prioritization techniques that can still be improved especially in data used and execution process for each approach

Unifying regression testing with mutation testing

textSoftware testing is the most commonly used methodology for validating quality of software systems. Conceptually, testing is simple, but in practice, given the huge (practically infinite) space of inputs to test against, it requires solving a number of challenging problems, including evaluating and reusing tests efficiently and effectively as software evolves. While software testing research has seen much progress in recent years, many crucial bugs still evade state-of-the-art approaches and cause significant monetary losses and sometimes are responsible for loss of life. My thesis is that a unified, bi-dimensional, change-driven methodology can form the basis of novel techniques and tools that can make testing significantly more effective and efficient, and allow us to find more bugs at a reduced cost. We propose a novel unification of the following two dimensions of change: (1) real manual changes made by programmers, e.g., as commonly used to support more effective and efficient regression testing techniques; and (2) mechanically introduced changes to code or specifications, e.g., as originally conceived in mutation testing for evaluating quality of test suites. We believe such unification can lay the foundation of a scalable and highly effective methodology for testing and maintaining real software systems. The primary contribution of my thesis is two-fold. One, it introduces new techniques to address central problems in both regression testing (e.g., test prioritization) and mutation testing (e.g., selective mutation testing). Two, it introduces a new methodology that uses the foundations of regression testing to speed up mutation testing, and also uses the foundations of mutation testing to help with the fault localization problem raised in regression testing. The central ideas are embodied in a suite of prototype tools. Rigorous experimental evaluation is used to validate the efficacy of the proposed techniques using a variety of real-world Java programs.Electrical and Computer Engineerin

How Time-Fault Ratio helps in Test Case Prioritization for Regression Testing

Regression testing analyzes whether the maintenance of the software has adversely affected its normal functioning. Regression testing is generally performed under the strict time constraints. Due to limited time budget, it is not possible to test the software with all available test cases. Thus, the reordering of the test cases, on the basis of their effectiveness, is always needed. A test prioritization technique, which prioritizes the test cases on the basis of their Time -Fault Ratio (TFR), has been proposed in this paper. The technique tends to maximize the fault detection as the faults are exposed in the ascending order of their detection times. The proposed technique may be used at any stage of software development

Variable-Based Fault Localization via Enhanced Decision Tree

Fault localization, aiming at localizing the root cause of the bug under repair, has been a longstanding research topic. Although many approaches have been proposed in the last decades, most of the existing studies work at coarse-grained statement or method levels with very limited insights about how to repair the bug (granularity problem), but few studies target the finer-grained fault localization. In this paper, we target the granularity problem and propose a novel finer-grained variable-level fault localization technique. Specifically, we design a program-dependency-enhanced decision tree model to boost the identification of fault-relevant variables via discriminating failed and passed test cases based on the variable values. To evaluate the effectiveness of our approach, we have implemented it in a tool called VARDT and conducted an extensive study over the Defects4J benchmark. The results show that VARDT outperforms the state-of-the-art fault localization approaches with at least 247.8% improvements in terms of bugs located at Top-1, and the average improvements are 330.5%. Besides, to investigate whether our finer-grained fault localization result can further improve the effectiveness of downstream APR techniques, we have adapted VARDT to the application of patch filtering, where VARDT outperforms the state-of-the-art PATCH-SIM by filtering 26.0% more incorrect patches. The results demonstrate the effectiveness of our approach and it also provides a new way of thinking for improving automatic program repair techniques