View in EDS PDF Full Text

Evaluating Unit Testing Practices in R Packages.

Saved in:
Bibliographic Details
Title: Evaluating Unit Testing Practices in R Packages.
Authors: Vidoni, Melina1 melina.vidoni@rmit.edu.au
Source: ICSE: International Conference on Software Engineering. 5/22/2021, p1523-1534. 12p.
Subjects: Computer software packaging, Learning curve, Artificial intelligence, Computer software development, Software engineering
Abstract: Testing Technical Debt (TTD) occurs due to shortcuts (non-optimal decisions) taken about testing; it is the test dimension of technical debt. R is a package-based programming ecosystem that provides an easy way to install third-party code, datasets, tests, documentation and examples. This structure makes it especially vulnerable to TTD because errors present in a package can transitively affect all packages and scripts that depend on it. Thus, TTD can effectively become a threat to the validity of all analysis written in R that rely on potentially faulty code. This two-part study provides the first analysis in this area. First, 177 systematically-selected, open-source R packages were mined and analysed to address quality of testing, testing goals, and identify potential TTD sources. Second, a survey addressed how R package developers perceive testing and face its challenges (response rate of 19.4%). Results show that testing in R packages is of low quality; the most common smells are inadequate and obscure unit testing, improper asserts, inexperienced testers and improper test design. Furthermore, skilled R developers still face challenges such as time constraints, emphasis on development rather than testing, poor tool documentation and a steep learning curve. [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 155538791
AccessLevel: 6
PubType: Conference
PubTypeId: conference
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Evaluating Unit Testing Practices in R Packages.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Vidoni%2C+Melina%22">Vidoni, Melina</searchLink><relatesTo>1</relatesTo><i> melina.vidoni@rmit.edu.au</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 5/22/2021, p1523-1534. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Computer+software+packaging%22">Computer software packaging</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+curve%22">Learning curve</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Testing Technical Debt (TTD) occurs due to shortcuts (non-optimal decisions) taken about testing; it is the test dimension of technical debt. R is a package-based programming ecosystem that provides an easy way to install third-party code, datasets, tests, documentation and examples. This structure makes it especially vulnerable to TTD because errors present in a package can transitively affect all packages and scripts that depend on it. Thus, TTD can effectively become a threat to the validity of all analysis written in R that rely on potentially faulty code. This two-part study provides the first analysis in this area. First, 177 systematically-selected, open-source R packages were mined and analysed to address quality of testing, testing goals, and identify potential TTD sources. Second, a survey addressed how R package developers perceive testing and face its challenges (response rate of 19.4%). Results show that testing in R packages is of low quality; the most common smells are inadequate and obscure unit testing, improper asserts, inexperienced testers and improper test design. Furthermore, skilled R developers still face challenges such as time constraints, emphasis on development rather than testing, poor tool documentation and a steep learning curve. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=155538791
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/ICSE43902.2021.00136
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1523
    Subjects:
      – SubjectFull: Computer software packaging
        Type: general
      – SubjectFull: Learning curve
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Computer software development
        Type: general
      – SubjectFull: Software engineering
        Type: general
    Titles:
      – TitleFull: Evaluating Unit Testing Practices in R Packages.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Vidoni, Melina
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 22
              M: 05
              Text: 5/22/2021
              Type: published
              Y: 2021
          Titles:
            – TitleFull: ICSE: International Conference on Software Engineering
              Type: main
ResultId 1