MSR 2024
Mon 15 - Tue 16 April 2024 Lisbon, Portugal
co-located with ICSE 2024
Mon 15 Apr 2024 11:48 - 12:00 at Grande Auditório - Defects, Bugs and Issues Chair(s): Wesley Assunção

Infrastructure-as-Code (IaC) is an emerging software engineering practice that leverages source code to facilitate automated configuration of software systems’ infrastructure. Like other software artifacts, severe defects could occur in IaC manifests, which can result in breaking millions of online services. To help developers early identify and fix IaC defects, several research efforts have introduced IaC defect prediction models at the file level. However, the granularity of the proposed approaches remains relatively coarse. Thus, identifying IaC bugs is a non-trivial task since IaC files may have many lines to inspect. To alleviate this issue, we aim to predict IaC defects at the fine-grained level by focusing on IaC blocks, which are code units that encapsulate specific behaviours within the IaC file. We evaluated our approach by conducting an empirical study on 19 open-source Terraform-based projects. We applied 6 machine learning algorithms to study to what extent they can predict defective blocks based on a mixture of code, process, and change level metrics. The results indicate that the LightGBM model emerged as the best model, achieving an average of 0.21 in the MCC and 0.71 in the AUC scores. We found that the developer’s experience and the relative number of added lines tend to be the most important features. Additionally, we found that blocks belonging to the most frequent types are more defect-prone. Our defect prediction models have also shown sensitivity to concept drift. This indicates that IaC practitioners should regularly retrain the defect models. We also suggest that practitioners stay attentive to changes in the syntax of block attributes, as these tend to be prone to modifications over time by IaC tool builders.

Mon 15 Apr

Displayed time zone: Lisbon change

11:00 - 12:30
11:00
12m
Talk
Enhancing Performance Bug Prediction Using Performance Code Metrics
Technical Papers
Guoliang Zhao Computer Science of Queen's University, Stefanos Georgio , Safwat Hassan University of Toronto, Canada, Ying Zou Queen's University, Kingston, Ontario, Derek Truong IBM Canada, Toby Corbin IBM UK
11:12
12m
Talk
An Investigation of Patch Porting Practices of the Linux Kernel Ecosystem
Technical Papers
Xingyu Li UC Riverside, Zheng Zhang UC Riverside, Zhiyun Qian University of California at Riverside, USA, Trent Jaeger UC Riverside, Chengyu Song University of California at Riverside, USA
11:24
12m
Talk
CrashJS: A NodeJS Benchmark for Automated Crash Reproduction
Technical Papers
Philip Oliver Victoria University of Wellington, Jens Dietrich Victoria University of Wellington, Craig Anslow Victoria University of Wellington, Michael Homer Victoria University of Wellington
11:36
12m
Talk
An Empirical Study on Just-in-time Conformal Defect Prediction
Technical Papers
Xhulja Shahini paluno - University of Duisburg-Essen, Andreas Metzger University of Duisburg-Essen, Klaus Pohl
11:48
12m
Talk
Fine-Grained Just-In-Time Defect Prediction at the Block Level in Infrastructure-as-Code (IaC)
Technical Papers
Mahi Begoug , Moataz Chouchen ETS, Ali Ouni ETS Montreal, University of Quebec, Eman Abdullah AlOmar Stevens Institute of Technology, Mohamed Wiem Mkaouer University of Michigan - Flint
12:00
4m
Talk
TrickyBugs: A Dataset of Corner-case Bugs in Plausible Programs
Data and Tool Showcase Track
Kaibo Liu Peking University, Yudong Han Peking University, Yiyang Liu Peking University, Zhenpeng Chen Nanyang Technological University, Jie M. Zhang King’s College London, Federica Sarro University College London, Gang Huang Peking University, Yun Ma Peking University
12:04
4m
Talk
GitBugs-Java: A Reproducible Java Benchmark of Recent Bugs
Data and Tool Showcase Track
André Silva KTH Royal Institute of Technology, Nuno Saavedra INESC-ID and IST, University of Lisbon, Martin Monperrus KTH Royal Institute of Technology
12:08
4m
Talk
A Dataset of Partial Program Fixes
Data and Tool Showcase Track
Dirk Beyer LMU Munich, Lars Grunske Humboldt-Universität zu Berlin, Matthias Kettl LMU Munich, Marian Lingsch-Rosenfeld LMU Munich, Moeketsi Raselimo Humboldt-Universität zu Berlin
12:12
4m
Talk
BugsPHP: A dataset for Automated Program Repair in PHP
Data and Tool Showcase Track
K.D. Pramod University of Moratuwa, Sri Lanka, W.T.N. De Silva University of Moratuwa, Sri Lanka, W.U.K. Thabrew University of Moratuwa, Sri Lanka, Ridwan Salihin Shariffdeen National University of Singapore, Sandareka Wickramanayake University of Moratuwa, Sri Lanka
Pre-print
12:16
4m
Talk
AW4C: A Commit-Aware C Dataset for Actionable Warning Identification
Data and Tool Showcase Track
Zhipeng Liu , Meng Yan Chongqing University, Zhipeng Gao Shanghai Institute for Advanced Study of Zhejiang University, dong li , Xiaohong Zhang Chongqing University, Dan Yang Chongqing University
12:20
5m
Talk
Predicting the Impact of Crashes Across Release Channels
Industry Track
Suhaib Mujahid Mozilla, Diego Costa Concordia University, Canada, Marco Castelluccio Mozilla
12:25
5m
Talk
Zero Shot Learning based Alternatives for Class Imbalanced Learning Problem in Enterprise Software Defect Analysis
Industry Track
Sangameshwar Patil Dept. of CSE, IIT Madras and TRDDC, TCS, B Ravindran IITM