What we have learned about fighting defects

By F. Shull, Victor R. Basili, Barry W. Boehm, A. W. Brown, P. Costa, M. Lindvall, D. Port, I. Rus, Roseanne Tesoriero, M. Zelkowitz and 1 more. Proceedings Eighth IEEE Symposium on Software Metrics, 2002

298 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/cc8b7bf2a382b8a9ea3c38b7dc2e792c6373e696

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. Jaechang Nam
2. Sunghun Kim
3. Tim Menzies
4. Wei Fu
5. Lin Tan
6. Claes Wohlin
7. Aybüke Aurum
8. Burak Turhan

In Software

1. Jaechang Nam
2. Sunghun Kim
3. Tim Menzies
4. Wei Fu
5. Lin Tan
6. Claes Wohlin
7. Aybüke Aurum
8. Burak Turhan

Citations in the corpus

• Data Mining Static Code Attributes to Learn Defect Predictors (2007, background): “This potential conflation can now be removed since it is now possible to define a baseline experiment using public-domain data sets which different researchers can use to compare their techniques.”
• Heterogeneous Defect Prediction (2015, background): “For example, a panel at IEEE Metrics 2002 [39] concluded that manual software reviews can find 60 percent of defects.”
• Defect prediction from static code features: current results, limitations, new approaches (2010, background): “A panel at IEEE Metrics 2002 (Shull et al. 2002) concluded that manual software reviews can find ≈60% of defects.”
• Empirical Findings in Agile Methods (2002, background): “[9] The rise of Agile Methods provides a fruitful area for such empirical research.”
• Engineering and Managing Software Requirements (2005, methodology): “Both likelihood and impact must be considered when determining the level of risk of an item or activity [44].”