An empirical validation of software cost estimation models
Communications of the ACM
Analyzing Regression Test Selection Techniques
IEEE Transactions on Software Engineering
An empirical study of regression test selection techniques
Proceedings of the 20th international conference on Software engineering
Building Knowledge through Families of Experiments
IEEE Transactions on Software Engineering
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Regression test selection for Java software
OOPSLA '01 Proceedings of the 16th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Preliminary guidelines for empirical research in software engineering
IEEE Transactions on Software Engineering
Automating Impact Analysis and Regression Test Selection Based on UML Designs
ICSM '02 Proceedings of the International Conference on Software Maintenance (ICSM'02)
Using Spanning Sets for Coverage Testing
IEEE Transactions on Software Engineering
Finding the Right Data for Software Cost Modeling
IEEE Software
Journal of Systems and Software
Search Algorithms for Regression Test Case Prioritization
IEEE Transactions on Software Engineering
Cross versus Within-Company Cost Estimation Studies: A Systematic Review
IEEE Transactions on Software Engineering
Evaluating guidelines for reporting empirical software engineering studies
Empirical Software Engineering
Guidelines for conducting and reporting case study research in software engineering
Empirical Software Engineering
A study of the uniqueness of source code
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
Understanding context: creating a lasting impact in experimental software engineering research
Proceedings of the FSE/SDP workshop on Future of software engineering research
How developers use the dynamic features of programming languages: the case of smalltalk
Proceedings of the 8th Working Conference on Mining Software Repositories
Semistructured merge: rethinking merge in revision control systems
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
On the congruence of modularity and code coupling
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Regression testing minimization, selection and prioritization: a survey
Software Testing, Verification & Reliability
Proceedings of the 34th International Conference on Software Engineering
BugRedux: reproducing field failures for in-house debugging
Proceedings of the 34th International Conference on Software Engineering
Temporal analysis of API usage concepts
Proceedings of the 34th International Conference on Software Engineering
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One of the goals of software engineering research is to achieve generality: Are the phenomena found in a few projects reflective of others? Will a technique perform as well on projects other than the projects it is evaluated on? While it is common sense to select a sample that is representative of a population, the importance of diversity is often overlooked, yet as important. In this paper, we combine ideas from representativeness and diversity and introduce a measure called sample coverage, defined as the percentage of projects in a population that are similar to the given sample. We introduce algorithms to compute the sample coverage for a given set of projects and to select the projects that increase the coverage the most. We demonstrate our technique on research presented over the span of two years at ICSE and FSE with respect to a population of 20,000 active open source projects monitored by Ohloh.net. Knowing the coverage of a sample enhances our ability to reason about the findings of a study. Furthermore, we propose reporting guidelines for research: in addition to coverage scores, papers should discuss the target population of the research (universe) and dimensions that potentially can influence the outcomes of a research (space).