Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The Build-Time Software Architecture View
ICSM '01 Proceedings of the IEEE International Conference on Software Maintenance (ICSM'01)
Detection of Logical Coupling Based on Product Release History
ICSM '98 Proceedings of the International Conference on Software Maintenance
Imposing a Memory Management Discipline on Software Deployment
Proceedings of the 26th International Conference on Software Engineering
Use of relative code churn measures to predict system defect density
Proceedings of the 27th international conference on Software engineering
Beyond source code: the importance of other artifacts in software development (a case study)
Journal of Systems and Software - Special issue: Selected papers from the 4th source code analysis and manipulation (SCAM 2004) workshop
Using Software Dependencies and Churn Metrics to Predict Field Failures: An Empirical Case Study
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Mining Software Repositories to Study Co-Evolution of Production & Test Code
ICST '08 Proceedings of the 2008 International Conference on Software Testing, Verification, and Validation
Kode vicious: System changes and side effects
Communications of the ACM - A Direct Path to Dependable Software
Using association rules to study the co-evolution of production & test code
MSR '09 Proceedings of the 2009 6th IEEE International Working Conference on Mining Software Repositories
Fair and balanced?: bias in bug-fix datasets
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
A Case Study of Bias in Bug-Fix Datasets
WCRE '10 Proceedings of the 2010 17th Working Conference on Reverse Engineering
Discovering fuzzy association rules with interest and conviction measures
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Proceedings of the 33rd International Conference on Software Engineering
The evolution of Java build systems
Empirical Software Engineering
Build code analysis with symbolic evaluation
Proceedings of the 34th International Conference on Software Engineering
SYMake: a build code analysis and refactoring tool for makefiles
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
1st international workshop on release engineering (RELENG 2013)
Proceedings of the 2013 International Conference on Software Engineering
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The build system of a software project is responsible for transforming source code and other development artifacts into executable programs and deliverables. Similar to source code, build system specifications require maintenance to cope with newly implemented features, changes to imported Application Program Interfaces (APIs), and source code restructuring. In this paper, we mine the version histories of one proprietary and nine open source projects of different sizes and domain to analyze the overhead that build maintenance imposes on developers. We split our analysis into two dimensions: (1) Build Coupling, i.e., how frequently source code changes require build changes, and (2) Build Ownership, i.e., the proportion of developers responsible for build maintenance. Our results indicate that, despite the difference in scale, the build system churn rate is comparable to that of the source code, and build changes induce more relative churn on the build system than source code changes induce on the source code. Furthermore, build maintenance yields up to a 27% overhead on source code development and a 44% overhead on test development. Up to 79% of source code developers and 89% of test code developers are significantly impacted by build maintenance, yet investment in build experts can reduce the proportion of impacted developers to 22% of source code developers and 24% of test code developers.