Ruchika Malhotra

Information Systems · 2015

A researcher in the Science corpus: 1 paper; trusts 29 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 27 citations into it; #2,886 of 8,771 by trust here, #7,705 by citations
• Software — 24 citations into it; #404 of 1,227 by trust here, #1,054 by citations
• Computer Networks and Communications — 10 citations into it; #3,476 of 9,886 by trust here, #8,543 by citations
• Artificial Intelligence — 5 citations into it

Whom Ruchika Malhotra trusts overall

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

1. Shyam R. Chidamber
2. Tim Menzies
3. Cagatay Catal
4. Stefan Lessmann
5. Jianfeng Wen
6. Chris F. Kemerer
7. Thomas Zimmermann
8. Jeremy Greenwald
9. Bart Baesens
10. Marco D'Ambros

Who trusts Ruchika Malhotra overall

1. Guanjun Lin
2. Sheng Wen
3. Seyed Mohammad Ghaffarian
4. Qing-Long Han
5. Joffrey L. Leevy
6. Marija Cubric
7. Jun Zhang

Papers from Ruchika Malhotra's perspective

Ordered by how strongly the researchers Ruchika Malhotra trusts endorse each paper. Same corpus, a different order for every researcher.

1. Data Mining Static Code Attributes to Learn Defect Predictors (IEEE Transactions on Software Engineering)
2. Empirical validation of object-oriented metrics on open source software for fault prediction (IEEE Transactions on Software Engineering)
3. A Metrics Suite for Object Oriented Design (IEEE Trans. Software Eng.)
4. Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings (IEEE Transactions on Software Engineering)
5. Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems (IEEE Transactions on Software Engineering)
6. Predicting defect-prone software modules using support vector machines (Journal of Systems and Software)
7. A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction (2008 ACM/IEEE 30th International Conference on Software Engineering)
8. On the relative value of cross-company and within-company data for defect prediction (Empirical Software Engineering)
9. Reliability and validity in comparative studies of software prediction models (IEEE Transactions on Software Engineering)
10. Classifying Software Changes: Clean or Buggy? (IEEE Transactions on Software Engineering)

Their papers

• A systematic review of machine learning techniques for software fault prediction (2015) — 608 citations