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Diversity in software engineering research

Published: 18 August 2013 Publication History

Abstract

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).

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cover image ACM Conferences
ESEC/FSE 2013: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
August 2013
738 pages
ISBN:9781450322379
DOI:10.1145/2491411
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 18 August 2013

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Author Tags

  1. Coverage
  2. Diversity
  3. Representativeness
  4. Sampling

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Overall Acceptance Rate 112 of 543 submissions, 21%

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  • (2024)Detecting semantic conflicts with unit testsJournal of Systems and Software10.1016/j.jss.2024.112070214:COnline publication date: 1-Aug-2024
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