Automatic mining of source code repositories to improve bug finding techniques

By Chadd C. Williams, Jeffrey K. Hollingsworth. 2005

220 citations recorded by the source corpus.

Subfields: Information Systems, Software, 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 Information Systems

1. Sunghun Kim
2. Hongyu Zhang
3. Tao Xie
4. Jian Pei
5. Rongxin Wu
6. Liang Gong
7. Jun Xu
8. Mithun P. Acharya

In Software

1. Sunghun Kim
2. Tao Xie
3. Jian Pei
4. Hongyu Zhang
5. Rongxin Wu
6. Benjamin Livshits
7. Jun Xu
8. Mithun P. Acharya

In Program Analysis & Verification

1. Jian Pei
2. Jun Xu
3. Mithun P. Acharya
4. Tao Xie
5. Ahmed E. Hassan
6. Thomas Zimmermann
7. Benjamin Livshits
8. Hongyu Zhang

Citations in the corpus

• Dealing with noise in defect prediction (2011, background): “Since most automatic buggy change/file identifications are based on special keywords and links [11, 16, 28, 33, 36] in the change logs, this will lead to false negatives (missing some buggy changes) in the automatically identified data.”
• Classifying Software Changes: Clean or Buggy? (2008, background): “Williams and Hollingsworth use project histories to improve existing bug-finding tools [51].”
• The road ahead for Mining Software Repositories (2008, background): “Williams and Hollingsworth use historical changes to refine the order of warnings produced by static code checkers [63].”
• DynaMine: finding common error patterns by mining software revision histories (2005, background): “Recently, Williams and Hollingsworth also turned to­wards mining function usage patterns from revision histories [36].”
• Mining API patterns as partial orders from source code: from usage scenarios to specifications (2007, methodology): “[25] C. C. Williams and J. K. Hollingsworth.”