37 citations · 43 across the 7 of their papers we have counts for
8 papers
Which Alert Removals are Beneficial?
Idan Amit
Context: Static analysis captures software engineering knowledge and alerts on possibly problematic patterns. Previous work showed that they indeed have predictive power for variou…
A Large Scale Survey of Motivation in Software Development and Analysis of its Validity
Idan Amit, Dror G. Feitelson
Context: Motivation is known to improve performance. In software development in particular, there has been considerable interest in the motivation of contributors to open source. O…
End to End Software Engineering Research
Idan Amit
End to end learning is machine learning starting in raw data and predicting a desired concept, with all steps done automatically. In software engineering context, we see it as star…
ComSum: Commit Messages Summarization and Meaning Preservation
Leshem Choshen, Idan Amit
We present ComSum, a data set of 7 million commit messages for text summarization. When documenting commits, software code changes, both a message and its summary are posted. We ga…
Follow Your Nose -- Which Code Smells are Worth Chasing?
Idan Amit, Nili Ben Ezra, Dror G. Feitelson
The common use case of code smells assumes causality: Identify a smell, remove it, and by doing so improve the code. We empirically investigate their fitness to this use. We presen…
A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits
Steffen Herbold, Alexander Trautsch, Benjamin Ledel +45
Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only…
The Corrective Commit Probability Code Quality Metric
Idan Amit, Dror G. Feitelson
We present a code quality metric, Corrective Commit Probability (CCP), measuring the probability that a commit reflects corrective maintenance. We show that this metric agrees with…
Machine Learning in Cyber-Security - Problems, Challenges and Data Sets
Idan Amit, John Matherly, William Hewlett +3
We present cyber-security problems of high importance. We show that in order to solve these cyber-security problems, one must cope with certain machine learning challenges. We prov…