Conference paper Open Access

On the Effectiveness of Manual and Automatic Unit Test Generation: Ten Years Later

Domenico Serra; Giovanni Grano; Fabio Palomba; Filomena Ferrucci; Harald C. Gall; Alberto Bacchelli


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.2595232</identifier>
  <creators>
    <creator>
      <creatorName>Domenico Serra</creatorName>
      <affiliation>University of Salerno</affiliation>
    </creator>
    <creator>
      <creatorName>Giovanni Grano</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8207-3259</nameIdentifier>
      <affiliation>University of Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Fabio Palomba</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9337-5116</nameIdentifier>
      <affiliation>University of Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Filomena Ferrucci</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0975-8972</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Harald C. Gall</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3874-5628</nameIdentifier>
      <affiliation>University of Zurich</affiliation>
    </creator>
    <creator>
      <creatorName>Alberto Bacchelli</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0193-6823</nameIdentifier>
      <affiliation>University of Zurich</affiliation>
    </creator>
  </creators>
  <titles>
    <title>On the Effectiveness of Manual and Automatic Unit Test Generation: Ten Years Later</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Software Testing</subject>
    <subject>Automatic Test Case Generation</subject>
    <subject>Empirical Studies</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-05-26</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2595232</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2595231</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/empirical-software-engineering</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Good unit tests play a paramount role when it comes to foster and evaluate software quality.&amp;nbsp;However, writing effective tests is an extremely costly and time consuming practice.&amp;nbsp;To reduce such a burden for developers, researchers devised ingenious techniques to automatically generate test suite for existing code bases.&amp;nbsp;Nevertheless, how automatically generated test cases fare against manually written ones is an open research question.&lt;/p&gt;

&lt;p&gt;In 2008, Bacchelli et al.&amp;nbsp;conducted an initial case study comparing automatic and manually generated test suites.&amp;nbsp;Since in the last ten years we have witnessed a huge amount of work on novel approaches and tools for automatic test generation, in this paper we revise their study using current tools as well as complementing their research method by evaluating these tools&amp;#39; ability in finding regressions.&lt;/p&gt;</description>
    <description descriptionType="Other">Preprint of the publication appeared in the proceedings of the 16th International Conference on Mining Software Repositories (MSR 2019), Montréal, Canada, 2019.</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100001711</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/SNSF/Careers/PP00P2_170529/">PP00P2_170529</awardNumber>
      <awardTitle>Data-driven Contemporary Code Review</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
168
106
views
downloads
All versions This version
Views 168168
Downloads 106106
Data volume 17.3 MB17.3 MB
Unique views 159159
Unique downloads 9999

Share

Cite as