M
Mario Linares-Vasquez
Researcher at University of Los Andes
Publications - 101
Citations - 4814
Mario Linares-Vasquez is an academic researcher from University of Los Andes. The author has contributed to research in topics: Android (operating system) & Source code. The author has an hindex of 37, co-authored 96 publications receiving 3876 citations. Previous affiliations of Mario Linares-Vasquez include College of William & Mary & National University of Colombia.
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Proceedings ArticleDOI
Toward deep learning software repositories
TL;DR: This work motivate deep learning for software language modeling, highlighting fundamental differences between state-of-the-practice software language models and connectionist models, and proposes avenues for future work, where deep learning can be brought to bear to support model-based testing, improve software lexicons, and conceptualize software artifacts.
Proceedings ArticleDOI
API change and fault proneness: a threat to the success of Android apps
Mario Linares-Vasquez,Gabriele Bavota,Carlos Bernal-Cardenas,Massimiliano Di Penta,Rocco Oliveto,Denys Poshyvanyk +5 more
TL;DR: A study analyzing how the fault- and change-proneness of APIs used by 7,097 (free) Android apps relates to applications' lack of success, estimated from user ratings.
Proceedings ArticleDOI
Mining energy-greedy API usage patterns in Android apps: an empirical study
Mario Linares-Vasquez,Gabriele Bavota,Carlos Bernal-Cardenas,Rocco Oliveto,Massimiliano Di Penta,Denys Poshyvanyk +5 more
TL;DR: This work presents the largest to date quantitative and qualitative empirical investigation into the categories of API calls and usage patterns that—in the context of the Android development framework—exhibit particularly high energy consumption profiles.
Journal ArticleDOI
The Impact of API Change- and Fault-Proneness on the User Ratings of Android Apps
Gabriele Bavota,Mario Linares-Vasquez,Carlos Bernal-Cardenas,Massimiliano Di Penta,Rocco Oliveto,Denys Poshyvanyk +5 more
TL;DR: The results of the studies indicate that apps having high user ratings use APIs that are less fault- and change-prone than the APIs used by low rated apps.
Proceedings ArticleDOI
User reviews matter! Tracking crowdsourced reviews to support evolution of successful apps
Fabio Palomba,Mario Linares-Vasquez,Gabriele Bavota,Rocco Oliveto,Massimiliano Di Penta,Denys Poshyvanyk,Andrea De Lucia +6 more
TL;DR: This paper devise an approach, named CRISTAL, for tracing informative crowd reviews onto source code changes, and for monitoring the extent to which developers accommodate crowd requests and follow-up user reactions as reflected in their ratings.