S
Sagar Sen
Researcher at Simula Research Laboratory
Publications - 74
Citations - 1879
Sagar Sen is an academic researcher from Simula Research Laboratory. The author has contributed to research in topics: Computer science & Model transformation. The author has an hindex of 21, co-authored 62 publications receiving 1637 citations. Previous affiliations of Sagar Sen include University of Rennes & New Jersey Institute of Technology.
Papers
More filters
Proceedings ArticleDOI
Automated and Scalable T-wise Test Case Generation Strategies for Software Product Lines
TL;DR: This work proposes a scalable toolset using Alloy to automatically generate test cases satisfying T-wise from SPL models, and defines strategies to split T- wise combinations into solvable subsets.
Journal ArticleDOI
Naming the pain in requirements engineering
D. Méndez Fernández,Stefan Wagner,Marcos Kalinowski,Michael Felderer,P. Mafra,Antonio Vetro,Tayana Conte,Marie-Therese Christiansson,Desmond Greer,Casper Lassenius,Tomi Männistö,M. Nayabi,Markku Oivo,Birgit Penzenstadler,Dietmar Pfahl,Rafael Prikladnicki,Guenther Ruhe,André Schekelmann,Sagar Sen,Rodrigo O. Spínola,Ahmet Tuzcu,J. L. de la Vara,Roel Wieringa +22 more
TL;DR: The Naming the Pain in Requirements Engineering (NaPiRE) initiative as discussed by the authors is a family of surveys on the status quo and problems in practical requirements engineering (RE) in 10 countries in various domains.
Journal ArticleDOI
Robust fuzzy clustering of relational data
Rajesh N. Dave,Sagar Sen +1 more
TL;DR: Based on the constrained minimization, a noise resistant, FRC algorithm is derived which works well for all types of non-Euclidean dissimilarity data, and it is shown that the extra computations for data expansion required by the NERFCM algorithm are not necessary.
Proceedings ArticleDOI
Test Case Prioritization for Continuous Regression Testing: An Industrial Case Study
TL;DR: The results show that the test cases prioritized using ROCKET (Prioritization for Continuous Regression Testing) provide faster fault detection, and increase regression fault detection rate, revealing 30% more faults for 20% of the test suite executed, comparing to manually prioritized test cases.
Journal ArticleDOI
Pairwise testing for software product lines: comparison of two approaches
TL;DR: This paper reports the experience on applying t-wise techniques for SPL with two independent toolsets developed by the authors, and derives useful insights for pairwise and t- Wise testing of product lines.