Marco D'Ambros

Information Systems · 2010–2011

A researcher in the Science corpus: 2 papers; trusts 34 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 47 citations into it; #1,783 of 8,771 by trust here, #3,640 by citations
• Software — 39 citations into it; #237 of 1,227 by trust here, #418 by citations
• Computer Networks and Communications — 16 citations into it; #2,142 of 9,886 by trust here, #3,993 by citations
• Artificial Intelligence — 13 citations into it

Whom Marco D'Ambros trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. N. Nagappan
2. Thomas Zimmermann
3. Shyam R. Chidamber
4. Ahmed E. Hassan
5. K. El Emam
6. Andrian Marcus
7. Thomas Ball
8. Igor Kononenko
9. Ramanath Subramanyam
10. Tim Menzies

Who trusts Marco D'Ambros overall

1. Chakkrit Kla Tantithamthavorn
2. Shane McIntosh
3. Ahmed E. Hassan
4. Jaechang Nam
5. Ken-ichi Matsumoto
6. Baljinder Ghotra
7. Xinli Yang
8. Peng He
9. Ruchika Malhotra
10. Baishakhi Ray

Papers from Marco D'Ambros's perspective

Ordered by how strongly the researchers Marco D'Ambros trusts endorse each paper. Same corpus, a different order for every researcher.

1. Predicting Fault Incidence Using Software Change History (IEEE Trans. Software Eng.)
2. Predicting the location and number of faults in large software systems (IEEE Transactions on Software Engineering)
3. Data Mining Static Code Attributes to Learn Defect Predictors (IEEE Transactions on Software Engineering)
4. Empirical validation of object-oriented metrics on open source software for fault prediction (IEEE Transactions on Software Engineering)
5. Mining metrics to predict component failures (International Conference on Software Engineering)
6. Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects (IEEE Trans. Software Eng.)
7. A Metrics Suite for Object Oriented Design (IEEE Trans. Software Eng.)
8. Fair and balanced?: bias in bug-fix datasets (ESEC/SIGSOFT FSE)
9. The prediction of faulty classes using object-oriented design metrics (Journal of Systems and Software)
10. Use of relative code churn measures to predict system defect density (Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005.)

Their papers

• Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011) — 608 citations
• An extensive comparison of bug prediction approaches (2010) — 640 citations