A Factor Graph Model for Software Bug Finding

By Theodore Kremenek, Andrew Y. Ng, Dawson R. Engler. 2007

27 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods, Program Analysis & Verification

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Citations in the corpus

Structured Generative Models of Natural Source Code (2014, background): “Other related applications include finding bugs (Kremenek et al., 2007), mining and suggesting API usage patterns (Bruch et al.”
Bayesian specification learning for finding API usage errors (2017, methodology): “Kremenek et al. [32, 33] use factor graphs constructed using static analysis to learn specifications on resource allocation and release.”
Typilus: neural type hints (2020, background): “Examples of include the early works of Kremenek et al. [40] and Raychev et al. [51] and variations of GNNs [5, 20, 33].”
Finding Likely Errors with Bayesian Specifications (2017, methodology): “There is also a body of work (Ammons et al. 2002; Beckman & Nori 2011; Kremenek et al. 2006; Livshits et al. 2009; Octeau et al. 2016; Gvero et al. 2013; Raychev et al. 2014) that uses machine learning techniques to learn probabilistic specifications from programs.”
Statistical Deobfuscation of Android Applications (2016, methodology): “Several works [22, 21, 16, 24] use graphical models to discover properties about programs such as function specifications and invariants.”
Learning a strategy for adapting a program analysis via bayesian optimisation (2015, background): “Another application of machine learning techniques is to encode soft constraints about program specifications in terms of a probabilistic model, and to infer a highly likely specification of a given program by performing a probabilistic inference on the model [2, 11, 13, 22].”