Ripon K. Saha
Fujitsu
35 Papers
164 Citations
Ripon K. Saha is an academic researcher from Fujitsu. The author has contributed to research in topics: Software bug & Computer science. The author has an hindex of 17, co-authored 35 publications. Previous affiliations of Ripon K. Saha include University of Saskatchewan & University of Texas at Austin.
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Papers
Improving bug localization using structured information retrieval
Ripon K. Saha,Matthew Lease,Sarfraz Khurshid,Dewayne E. Perry +3 more
- 11 Nov 2013
TL;DR: This work provides a thorough grounding of IR-based bug localization research in fundamental IR theoretical and empirical knowledge and practice and presents BLUiR, which embodies this insight, requires only the source code and bug reports, and takes advantage of bug similarity data if available.
ELIXIR: effective object oriented program repair
Ripon K. Saha,Yingjun Lyu,Hiroaki Yoshida,Mukul R. Prasad +3 more
- 30 Oct 2017
TL;DR: ELIXIR is able to increase the number of correctly repaired bugs in Defects4J and in Bugs.jar by 85% and significantly out-performing other state-of-the-art repair techniques including ACS, HD-Repair, NOPOL, PAR, and jGenProg.
251
Bugs.jar: a large-scale, diverse dataset of real-world Java bugs
Ripon K. Saha,Yingjun Lyu,Wing Lam,Hiroaki Yoshida,Mukul R. Prasad +4 more
- 28 May 2018
TL;DR: Bugs.jar is a large-scale dataset for research in automated debugging, patching, and testing of Java programs, comprised of 1,158 bugs and patches, drawn from 8 large, popular opensource Java projects, spanning 8 diverse and prominent application categories.
217
Fuzz testing based data augmentation to improve robustness of deep neural networks
Xiang Gao,Ripon K. Saha,Mukul R. Prasad,Abhik Roychoudhury +3 more
- 27 Jun 2020
TL;DR: This paper proposes a technique that re-purposes software testing methods, specifically mutation-based fuzzing, to augment the training data of DNNs, with the objective of enhancing their robustness, and casts the DNN data augmentation problem as an optimization problem.
162
An information retrieval approach for regression test prioritization based on program changes
Ripon K. Saha,Lingming Zhang,Sarfraz Khurshid,Dewayne E. Perry +3 more
- 16 May 2015
TL;DR: A new approach is introduced, REPiR, to address the problem of regression test prioritization by reducing it to a standard Information Retrieval problem such that the differences between two program versions form the query and the tests constitute the document collection.