activity
20172022
most citedWatch out for Extrinsic Bugs! A Case Study of their Impact in Just-In-Time Bug Prediction Models on the OpenStack project

38 citations · 60 across the 6 of their papers we have counts for

collaborators

7 papers

cs.SE20221 cited

ApacheJIT: A Large Dataset for Just-In-Time Defect Prediction

Hossein Keshavarz, Meiyappan Nagappan

In this paper, we present ApacheJIT, a large dataset for Just-In-Time defect prediction. ApacheJIT consists of clean and bug-inducing software changes in popular Apache projects. A…

cs.SE20212 cited

On the Relationship Between the Developer's Perceptible Race and Ethnicity and the Evaluation of Contributions in OSS

Reza Nadri, Gema Rodríguez-Pérez, Meiyappan Nagappan

Open Source Software (OSS) projects are typically the result of collective efforts performed by developers with different backgrounds. Although the quality of developers' contribut…

cs.SE202138 cited

Watch out for Extrinsic Bugs! A Case Study of their Impact in Just-In-Time Bug Prediction Models on the OpenStack project

Gema Rodriguez-Perez, Meiyappan Nagappan, Gregorio Robles

Intrinsic bugs are bugs for which a bug introducing change can be identified in the version control system of a software. In contrast, extrinsic bugs are caused by external changes…

cs.SE2020

Ammonia: An Approach for Deriving Project-specific Bug Patterns

Yoshiki Higo, Shinpei Hayashi, Hideaki Hata +1

Finding and fixing buggy code is an important and cost-intensive maintenance task, and static analysis (SA) is one of the methods developers use to perform it. SA tools warn develo…

cs.SE20191 cited

Exploiting Token and Path-based Representations of Code for Identifying Security-Relevant Commits

Achyudh Ram, Ji Xin, Meiyappan Nagappan +4

Public vulnerability databases such as CVE and NVD account for only 60% of security vulnerabilities present in open-source projects, and are known to suffer from inconsistent quali…

cs.CL2018

Supervised Sentiment Classification with CNNs for Diverse SE Datasets

Achyudh Ram, Meiyappan Nagappan

Sentiment analysis, a popular technique for opinion mining, has been used by the software engineering research community for tasks such as assessing app reviews, developer emotions…