Extended Comprehensive Study of Association Measures for Fault Localization

To cite the data package, please use the following citation: Lucia, L., Lo, D., Jiang, L., Thung, F., & Budi, A. (2014). Data from: Extended Comprehensive Study of Association Measures for Fault Localization. InK Repository at Singapore Management University. http://ink.library.smu.edu.sg/sis_research/1818</p

Spectrum-based Fault Localization Techniques Application on Multiple-Fault Programs: A Review

Software fault localization is one of the most tedious and costly activities in program debugging in the endeavor to identify faults locations in a software program. In this paper, the studies that used spectrum-based fault localization (SBFL) techniques that makes use of different multiple fault localization debugging methods such as one-bug-at-a-time (OBA) debugging, parallel debugging, and simultaneous debugging in localizing multiple faults are classified and critically analyzed in order to extensively discuss the current research trends, issues, and challenges in this field of study. The outcome strongly shows that there is a high utilization of OBA debugging method, poor fault isolation accuracy, and dominant use of artificial faults that limit the existing techniques applicability in the software industry

Ranking Policy Decisions

Policies trained via Reinforcement Learning (RL) are often needlessly complex, making them difficult to analyse and interpret. In a run with $n$ time steps, a policy will make $n$ decisions on actions to take; we conjecture that only a small subset of these decisions delivers value over selecting a simple default action. Given a trained policy, we propose a novel black-box method based on statistical fault localisation that ranks the states of the environment according to the importance of decisions made in those states. We argue that among other things, the ranked list of states can help explain and understand the policy. As the ranking method is statistical, a direct evaluation of its quality is hard. As a proxy for quality, we use the ranking to create new, simpler policies from the original ones by pruning decisions identified as unimportant (that is, replacing them by default actions) and measuring the impact on performance. Our experiments on a diverse set of standard benchmarks demonstrate that pruned policies can perform on a level comparable to the original policies. Conversely, we show that naive approaches for ranking policy decisions, e.g., ranking based on the frequency of visiting a state, do not result in high-performing pruned policies

Information Retrieval and Spectrum Based Bug Localization: Better Together

Debugging often takes much effort and resources. To help developers debug, numerous information retrieval (IR)-based and spectrum-based bug localization techniques have been proposed. IR-based techniques process textual infor-mation in bug reports, while spectrum-based techniques pro-cess program spectra (i.e., a record of which program el-ements are executed for each test case). Both eventually generate a ranked list of program elements that are likely to contain the bug. However, these techniques only con-sider one source of information, either bug reports or pro-gram spectra, which is not optimal. To deal with the limita-tion of existing techniques, in this work, we propose a new multi-modal technique that considers both bug reports and program spectra to localize bugs. Our approach adaptively creates a bug-specific model to map a particular bug to its possible location, and introduces a novel idea of suspicious words that are highly associated to a bug. We evaluate our approach on 157 real bugs from four software systems, and compare it with a state-of-the-art IR-based bug localization method, a state-of-the-art spectrum-based bug localization method, and three state-of-the-art multi-modal feature loca-tion methods that are adapted for bug localization. Experi-ments show that our approach can outperform the baselines by at least 47.62%, 31.48%, 27.78%, and 28.80 % in terms of number of bugs successfully localized when a developer in

Diversity Maximization Speedup for Localizing Faults in Single-Fault and Multi-Fault Programs

Advance online 06/09/2014</p

Combining Fault Localization with Information Retrieval: an Analysis of Accuracy and Performance for Bug Finding

Debugging is a key activity in the software development process. It has been used extensively by developers to attempt to localize faults, while enhancing the quality and performance of software in general. There has been a significant amount of study in developing and enhancing fault localization techniques, which are used in assisting developers to locate faults within a body of code. However, identifying fault locations using individual techniques is not always effective; combining different techniques, which represent distinct forms of analysis, might help to overcome this issue. There has been a very limited amount of research that suggests that combining more than one approach to fault localization may have benefits, principally because information from different sources is included in the localization process. In this thesis, I attempt to more precisely address the question of whether combining different fault localization techniques can more effectively and efficiently find faults in code, when contrasted with a single technique. To answer this, I have carried out experiments that combine the use of three fault localization techniques: Information Retrieval (IR), Spectrum Based Fault Localization (SBFL), and Text Based Search. These techniques are representative of both dynamic and static fault localization. My hypothesis is that a combination of dynamic and static fault localization analysis can assist developers in better fault localization. I have evaluated the various combinations of techniques in identifying faults against real-world programs, Defects4j, where 395 faults and bug reports have been analyzed. The experimental results demonstrate that the combination of three techniques (SBFL, Text Search, and IR) is the most accurate, with 86.84% accuracy for 343 faults located from a total of 395. This finding contributes positively towards concretely recommending techniques for assisting developers in locating faults in code. Guidelines are provided on which combination of techniques, with maximal accuracy of result, should be applied especially when there is no prior knowledge about the fault