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Nachiappan Nagappan
Researcher at Microsoft
Publications - 171
Citations - 13728
Nachiappan Nagappan is an academic researcher from Microsoft. The author has contributed to research in topics: Software & Software quality. The author has an hindex of 51, co-authored 171 publications receiving 11934 citations. Previous affiliations of Nachiappan Nagappan include Facebook & Indraprastha Institute of Information Technology.
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Proceedings ArticleDOI
Use of relative code churn measures to predict system defect density
Nachiappan Nagappan,Thomas Ball +1 more
TL;DR: A technique for early prediction of system defect density using a set of relative code churn measures that relate the amount of churn to other variables such as component size and the temporal extent of churn, which shows that while absolute measures of code chum are poor predictors of defect density, these measures are highly predictive of defectdensity.
Proceedings ArticleDOI
Mining metrics to predict component failures
TL;DR: Using principal component analysis on the code metrics, this work built regression models that accurately predict the likelihood of post-release defects for new entities and can be generalized to arbitrary projects.
Proceedings ArticleDOI
Understanding network failures in data centers: measurement, analysis, and implications
TL;DR: The first large-scale analysis of failures in a data center network is presented, finding that data center networks show high reliability, commodity switches such as ToRs and AggS are highly reliable, and network redundancy is only 40% effective in reducing the median impact of failure.
Proceedings ArticleDOI
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
TL;DR: This paper studied cross-project defect prediction models on a large scale and identified factors that do influence the success of cross- project predictions, and derived decision trees that can provide early estimates for precision, recall, and accuracy before a prediction is attempted.
Proceedings ArticleDOI
Software engineering for machine learning: a case study
Saleema Amershi,Andrew Begel,Christian Bird,Robert DeLine,Harald C. Gall,Ece Kamar,Nachiappan Nagappan,Besmira Nushi,Thomas Zimmermann +8 more
TL;DR: A study conducted on observing software teams at Microsoft as they develop AI-based applications finds that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace.