Mutation analysis can effectively capture the dependency between source code and test results. This has been exploited by Mutation Based Fault Localisation (MBFL) techniques. However, MBFL techniques suffer from the need to expend the high cost of mutation analysis after the observation of failures, which may present a challenge for its practical adoption. We introduce SIMFL (Statistical Inference for Mutation-based Fault Localisation), an MBFL technique that allows users to perform the mutation analysis in advance against an earlier version of the system. SIMFL uses mutants as artificial faults and aims to learn the failure patterns among test cases against different locations of mutations. Once a failure is observed, SIMFL requires either almost no or very small additional cost for analysis, depending on the used inference model. An empirical evaluation of SIMFL using 355 faults in Defects4J shows that SIMFL can successfully localise up to 103 faults at the top, and 152 faults within the top five, on par with state-of-the-art alternatives. The cost of mutation analysis can be further reduced by mutation sampling: SIMFL retains over 80 of its localisation accuracy at the top rank when using only 10 of generated mutants, compared to results obtained without sampling.
was initially formulated based on Bayesian analysis to infer likely fault locations given test information. In the context of fault localisation, @cite_2 have introduced , an SBFL technique that adopts Bayesian reasoning to generate candidate sets of multiple fault locations. To the best of our knowledge, is the first MBFL technique that uses Bayesian inference as well as other statistical inference techniques. Bayesian inference has been used in other forms of defect modelling. @cite_9 proposed and later summarized @cite_39 work on using Bayesian Belief Networks for software defect prediction. Similarly, Okutan and Yildiz @cite_34 used Bayesian modelling to predict defect proneness based on software metrics. However, like the other work on software defect prediction, the focus is on software quality, reliability, and the number of remaining defects, not, as for , on helping to locate specific faults based on test information.
@cite_9 @cite_34 @cite_39 @cite_2
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Reviews studies from 1991-2013 to assess application of ML techniques for SFP.Identifies seven categories of the ML techniques.Identifies 64 studies to answer the established research questions.Selects primary studies according to the quality assessment of the studies.Systematic literature review performs the following:Summarize ML techniques for SFP models.Assess performance accuracy and capability of ML techniques for constructing SFP models.Provide comparison between the ML and statistical techniques.Provide comparison of performance accuracy of different ML techniques.Summarize the strength and weakness of the ML techniques.Provides future guidelines to software practitioners and researchers. BackgroundSoftware fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults. MethodIn this study we perform a systematic review of studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the statistical techniques and other machine learning techniques. Further the strengths and weaknesses of machine learning techniques are summarized. ResultsIn this paper we have identified 64 primary studies and seven categories of the machine learning techniques. The results prove the prediction capability of the machine learning techniques for classifying module class as fault prone or not fault prone. The models using the machine learning techniques for estimating software fault proneness outperform the traditional statistical models. ConclusionBased on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers. However, the application of the machine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work. Bayesian network (BN) models can be used to predict the level of fault injection in the different phases of the software development process. This paper describes the techniques used by Motorola to develop BN fault prediction models using product, usage and process information. It also includes a procedure created to validate and calibrate the BN models. Statistical techniques are applied that provide a measurement of the quality of the predictions made by the models, and directions on how the models could be improved. The validation method described in this paper considers two alternative ways to produce an improved network. The first is based mainly on modifying the ranges of the existing nodes, adding interdependencies between them, and varying the weight values associated with each of the nodes that are used as inputs to the intermediate nodes of the BN model. The second possibility uses linear regression and principal component analysis to build the intermediate and output nodes of the network. The paper closes with some encouraging results, and outlines a number of unresolved questions. Copyright © 2006 John Wiley & Sons, Ltd. Fault diagnosis approaches can generally be categorized into spectrum-based fault localization (SFL, correlating failures with abstractions of program traces), and model-based diagnosis (MBD, logic reasoning over a behavioral model). Although MBD approaches are inherently more accurate than SFL, their high computational complexity prohibits application to large programs. We present a framework to combine the best of both worlds, coined BARINEL. The program is modeled using abstractions of program traces (as in SFL) while Bayesian reasoning is used to deduce multiple-fault candidates and their probabilities (as in MBD). A particular feature of BARINEL is the usage of a probabilistic component model that accounts for the fact that faulty components may fail intermittently. Experimental results on both synthetic and real software programs show that BARINEL typically outperforms current SFL approaches at a cost complexity that is only marginally higher. In the context of single faults this superiority is established by formal proof. Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software. Objective: We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models. Method: We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesize the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply. Results: The models that perform well tend to be based on simple modeling techniques such as Naive Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well. Conclusion: The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively.