SOBER: statistical model-based bug localization

By Chao Liu, Xifeng Yan, Long Fei, Jiawei Han, Samuel P. Midkiff. ESEC/FSE-13, 2005

438 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/68d4fae431e2b8f8b982f4ab689dd13dac345866

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Citations in the corpus

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].”
Statistical debugging: simultaneous identification of multiple bugs (2006, methodology): “Liu et al. (2005) present the SOBER algorithm, which aims to solve the single-bug problem using a test derived from one-sample hypothesis testing.”
Retrieval from software libraries for bug localization: a comparative study of generic and composite text models (2011, methodology): “The failing rules are then used to detect and categorize the bugs [14] [11].”
Learning to rank relevant files for bug reports using domain knowledge (2014, methodology): “To support fault localization, a number of approaches [9, 11, 16, 24] use runtime information that was generated for debugging.”
Are automated debugging techniques actually helping programmers? (2011, background): “, [1, 8] and approaches based on model checking in general), statement coverage [11], and predicate values [17,19]—and in the specific type of mining performed on such information.”
BugFix: A learning-based tool to assist developers in fixing bugs (2009, methodology): “Other approaches [17, 21, 24, 25] use runtime information to rank program entities in the order of their likelihood of being faulty.”
Statistical Debugging: A Hypothesis Testing-Based Approach (2006, background): “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].”
Spectrum-Based Multiple Fault Localization (2009, methodology): “SFL uses abstraction of program traces to correlate software component activity with program failures (a statistical approach) [4], [14], [20], [24], [29], [37].”
A model for spectra-based software diagnosis (2011, methodology): “An alternative approach to bug localization is to instrument certain predicates in the code rather than statement execution [Liblit et al. 2005; Liu et al. 2005].”
A practical evaluation of spectrum-based fault localization (2009, methodology): “Examples of other black box techniques are Nearest Neighbor (Renieris and Reiss, 2003), dynamic program slicing (Agrawal et al., 1993; Liu et al., 2005), Delta Debugging (Zeller, 2002), and D-slicing (Groce, 2004).”