L
Lionel C. Briand
Researcher at University of Luxembourg
Publications - 37
Citations - 1145
Lionel C. Briand is an academic researcher from University of Luxembourg. The author has contributed to research in topics: Computer science & Software development. The author has an hindex of 7, co-authored 37 publications receiving 933 citations. Previous affiliations of Lionel C. Briand include University of Navarra & University of Ottawa.
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Journal ArticleDOI
A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation
TL;DR: The intent is to aid future researchers doing empirical studies in SBST by providing an unbiased view of the body of empirical evidence and by guiding them in performing well-designed and executed empirical studies.
Journal ArticleDOI
How reuse influences productivity in object-oriented systems
TL;DR: The author's eight system study begins to define reuse benefits in an OO framework, most notably in terms of reduce defect density and rework as well as in increased productivity.
Proceedings ArticleDOI
Understanding and predicting the process of software maintenance releases
TL;DR: A predictive effort model was developed for the FDD's software maintenance release process and a set of lessons learned about the establishment of a measurement-based software maintenance improvement program are presented.
Journal ArticleDOI
Reinforcement Learning for Test Case Prioritization
TL;DR: This work model the sequential interactions between the CI environment and a test case prioritization agent as an RL problem, using three alternative ranking models, and shows that the best RL solutions provide a significant accuracy improvement over previous RL-based work, with prioritization strategies getting close to being optimal.
Proceedings ArticleDOI
Using Domain-specific Corpora for Improved Handling of Ambiguity in Requirements
TL;DR: In this article, the authors propose an automated approach that uses natural language processing for handling ambiguity in requirements, based on the automatic generation of a domain-specific corpus from Wikipedia, which leads to a significant positive improvement in the accuracy of ambiguity detection and interpretation.