Empirical evaluation of the tarantula automatic fault-localization technique

By James A. Jones, Mary Jean Harrold. International Conference on Automated Software Engineering, 2005

1,328 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/0bc25a572260f0b726ff6681d264c5684a462c24

How researchers see this paper

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In Information Systems

1. Lingming Zhang
2. Chun Xia
3. David Lo
4. Claire Le Goues
5. Yinlin Deng
6. Xifeng Yan
7. Westley Weimer
8. Jiawei Han

In Software

1. Lingming Zhang
2. Chun Xia
3. Claire Le Goues
4. Yinlin Deng
5. David Lo
6. Rui Abreu
7. Soren Dunn
8. Yves Le Traon

Citations in the corpus

• Practitioners' expectations on automated fault localization (2016, methodology): “Parnin and Orso [31] investigated the usability of a spectrumbased fault localization technique named Tarantula [17].”
• Evaluating and Improving Fault Localization (2017, methodology): “It has been very common to evaluate and compare fault localization techniques using manually-seeded artificial faults (e.g., Siemens set [3], [18], [33], [38]) or mutations (e.g., [12], [38], [44]) as a proxy to real faults .”
• The DStar Method for Effective Software Fault Localization (2014, methodology): “Because it is not possible to theoretically prove if one fault localization technique shall always be more effective than another, such empirical validation is typically the norm for fault localization studies [1], [3], [8], [23], [25], [31], [39], [40], [42].”
• Are automated debugging techniques actually helping programmers? (2011, background): “Second, a thorough empirical evaluation of Tarantula has shown that it can outperform other techniques [12].”
• On the Accuracy of Spectrum-based Fault Localization (2007, methodology): “(1), the coefficient used in the Tarantula fault localization tool as defined in Eq.”
• Ask the Mutants: Mutating Faulty Programs for Fault Localization (2014, methodology): “It has been widely adopted as an evaluation metric for FL techniques [13, 19, 31] as well as a theoretical framework that showed hierarchies between SBFL techniques [28, 29].”
• Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports (2012, methodology): “Unlike spectrum-based fault localization techniques [1, 18, 19, 22, 23], information retrieval (IR) based bug localization does not require program execution information (such as passing and failing traces).”
• An Evaluation of Similarity Coefficients for Software Fault Localization (2006, methodology): “The Tarantula system [12, 13] has been developed for the C language, and applies the technique of the previous section to statement hit spectra.”
• Are mutants a valid substitute for real faults in software testing? (2014, background): “To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.”
• TBar: revisiting template-based automated program repair (2019, methodology): “SBFL techniques rely on the ranking metrics (e.g., Trantula [15], Ochiai [2], Op2 [47], Barinel [3], Dstar[66]) to calculate the sus-piciousness of each statement.”
• A practical evaluation of spectrum-based fault localization (2009, methodology): “This second coefficient is interpreted as a brightness value for visualization purposes, but the experiments in (Jones and Harrold, 2005) indicate that sT can 2 In our experimental setup, we do not consider effects that carry over from one run to another, so conversely, if an error is detected, the…”
• A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each (2012, background): “GenProg focuses repair efforts on statements that are visited by the negative test cases, biased heavily towards those that are not also visited by positive test cases [3].”
• Metallaxis‐FL: mutation‐based fault localization (2015, methodology): “Researchers have used many coverage elements such as program statements [9], [10], [11] program branches [12], [8] program du-pairs [13] and possible combinations of them [8], [14].”
• A model for spectra-based software diagnosis (2011, methodology): “The Tarantula system [Jones and Harrold 2005] is credited as the first system to apply spectra-based ranking to software diagnosis (for an imperative language).”
• Statistical Debugging: A Hypothesis Testing-Based Approach (2006, background): “T HE last decade has witnessed great advances in fault localization techniques [1], [2], [3], [4], [5], [6], [7], [8], [9].”
• Agentless: Demystifying LLM-based Software Engineering Agents (2024, methodology): “Dynamic FL techniques mainly include spectrum-based FL (SBFL) [47, 25, 24] and mutation-based FL (MBFL) [77, 68, 63].”
• SQLizer: query synthesis from natural language (2017, methodology): “…earlier, the sketch refinement strategy in Sqlizer is inspired by prior work on fault localization [Ball et al. 2003; Groce and Visser 2003; Jones and Harrold 2005; Jones et al. 2002; Jose and Majumdar 2011a,b] and program repair [Goues et al. 2012; Long and Rinard 2015, 2016; Nguyen et…”
• Retrieval from software libraries for bug localization: a comparative study of generic and composite text models (2011, methodology): “A similar metric for fault detection , known as SCORE [12], can be used to indicate the proportion of a program that needs to be examined in order to locate a faulty statement.”