activity
20192022
most citedCode Smells in Machine Learning Systems

12 citations · 21 across the 6 of their papers we have counts for

collaborators

12 papers

cs.SE20222 cited

The Evolving Landscape of Software Performance Engineering

Gunnar Kudrjavets, Jeff Thomas, Nachiappan Nagappan

Satisfactory software performance is essential for the adoption and the success of a product. In organizations that follow traditional software development models (e.g., waterfall)…

cs.SE202212 cited

Code Smells in Machine Learning Systems

Jiri Gesi, Siqi Liu, Jiawei Li +6

As Deep learning (DL) systems continuously evolve and grow, assuring their quality becomes an important yet challenging task. Compared to non-DL systems, DL systems have more compl…

cs.SE20221 cited

Quantifying Daily Evolution of Mobile Software Based on Memory Allocator Churn

Gunnar Kudrjavets, Jeff Thomas, Aditya Kumar +2

The pace and volume of code churn necessary to evolve modern software systems present challenges for analyzing the performance impact of any set of code changes. Traditional method…

cs.SE20222 cited

The Unexplored Terrain of Compiler Warnings

Gunnar Kudrjavets, Aditya Kumar, Nachiappan Nagappan +1

The authors' industry experiences suggest that compiler warnings, a lightweight version of program analysis, are valuable early bug detection tools. Significant costs are associate…

cs.SE20214 cited

Can Program Synthesis be Used to Learn Merge Conflict Resolutions? An Empirical Analysis

Rangeet Pan, Vu Le, Nachiappan Nagappan +3

Forking structure is widespread in the open-source repositories and that causes a significant number of merge conflicts. In this paper, we study the problem of textual merge confli…

cs.SE2021

SoftNER: Mining Knowledge Graphs From Cloud Incidents

Manish Shetty, Chetan Bansal, Sumit Kumar +2

The move from boxed products to services and the widespread adoption of cloud computing has had a huge impact on the software development life cycle and DevOps processes. Particula…