Statistical Debugging of Sampled Programs

By Alice X. Zheng, Michael I. Jordan, Ben Liblit, A. Aiken. 2003

80 citations recorded by the source corpus.

Subfields: Evaluation & Benchmarks

How researchers see this paper

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

In Evaluation & Benchmarks

1. Charles Sutton
2. Miltiadis Allamanis
3. Andrew Y. Ng
4. Earl T. Barr
5. Premkumar T. Devanbu
6. A. Aiken
7. Michael I. Jordan
8. Alice X. Zheng

Citations in the corpus

• A Survey of Machine Learning for Big Code and Naturalness (2017, background): “e during its execution. Probabilistic models of source code could help developers, such as by filtering highly-improbable program states. Statistical debugging models, such as the work of Zheng et al. [198, 199] and Liblit et al. [117] are indicative of the possibilities within this area. Further add”
• Scalable statistical bug isolation (2005, methodology): “In previous work, we focused on techniques for lightweight instrumentation and sampling of program executions, but we also studied two preliminary algorithms for statistical debugg ing and presented experimental results on medium-size applicatio ns with a single bug [10, 16].”
• Feature selection, L1 vs. L2 regularization, and rotational invariance (2004, background): “Another example of learning using L1 regularization is found in (Zheng et al., 2004).”
• Statistical debugging: simultaneous identification of multiple bugs (2006, methodology): “Relatively straightforward, off-the-shelf techniques work well in this case (Zheng et al., 2004).”