7 citations · 19 across the 8 of their papers we have counts for
8 papers · 1 filter
Code to Comment "Translation": Data, Metrics, Baselining & Evaluation
David Gros, Hariharan Sezhiyan, Prem Devanbu +1
The relationship of comments to code, and in particular, the task of generating useful comments given the code, has long been of interest. The earliest approaches have been based o…
Deep Learning & Software Engineering: State of Research and Future Directions
Prem Devanbu, Matthew Dwyer, Sebastian Elbaum +6
Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was co…
Patching as Translation: the Data and the Metaphor
Yangruibo Ding, Baishakhi Ray, Premkumar Devanbu +1
Machine Learning models from other fields, like Computational Linguistics, have been transplanted to Software Engineering tasks, often quite successfully. Yet a transplanted model'…
Rebuttal to Berger et al., TOPLAS 2019
Baishakhi Ray, Prem Devanbu, Vladimir Filkov
Berger et al., published in TOPLAS 2019, is a critique of our 2014 FSE conference abstract and its archival version, the 2017 CACM paper: A Large-Scale Study of Programming Languag…
Are My Invariants Valid? A Learning Approach
Vincent J. Hellendoorn, Premkumar T. Devanbu, Oleksandr Polozov +1
Ensuring that a program operates correctly is a difficult task in large, complex systems. Enshrining invariants -- desired properties of correct execution -- in code or comments ca…
BugSwarm: Mining and Continuously Growing a Dataset of Reproducible Failures and Fixes
David A. Tomassi, Naji Dmeiri, Yichen Wang +5
Fault-detection, localization, and repair methods are vital to software quality; but it is difficult to evaluate their generality, applicability, and current effectiveness. Large,…