Fault localization using execution slices and dataflow tests

By H. Agrawal, J. R. Horgan, Saul London, W. E. Wong. 1995

Subfields: Program Analysis & Verification

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Program Analysis & Verification

1. James A. Jones
2. Mary Jean Harrold
3. Yihao Li
4. Ruizhi Gao
5. Baowen Xu
6. Rui Abreu
7. W. E. Wong
8. Ilya Sutskever

Citations in the corpus

• Evaluating Large Language Models Trained on Code (2021, methodology): “Early works used static or dynamic code analysis (Agrawal et al., 1995; Korel & Rilling, 1997), learned association rules (Jeffrey et al., 2009), and genetic programming (Goues et al., 2012) to debug faulty code.”
• Statistical Debugging: A Hypothesis Testing-Based Approach (2006, methodology): “Within dynamic analysis, most fault localization techniques are based on the contrast between failing and passing cases [4], [5], [6], [7], [8], [12], [20], [21], [33].”
• A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization (2013, background): “For example, one of the most widely adopted spectra is the execution slice [Agrawal et al. 1995; Wong and Qi 2006].”
• A Survey on Software Fault Localization (2016, methodology): “for spectrum-based fault localization, though this approach has subsequently been deemed ineffective [21], [185], [366].”
• Empirical evaluation of the tarantula automatic fault-localization technique (2005, methodology): “By removing the set of statements executed by a passed test case from the set of statements executed by a failed test case, their approach applies the technique of Agrawal and colleagues in [1], but has a specific technique for specifying which passed test case to use for this set difference.”
• 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].”
• Visualization of test information to assist fault localization (2002, methodology): “Agrawal, Horgan, London, and Wong presented a similar technique [1, 2] and an implementation, called xSlice, xSlice also presents a color mapping applied to the statements of the program under test.”