Zobrazit v EDS

Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality

Uloženo v:
Podrobná bibliografie
Název: Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality
Autoři: Furia, Carlo A, 1979, Torkar, Richard, 1971, Feldt, Robert, 1972
Zdroj: ACM Transactions on Software Engineering and Methodology. 31(3)
Témata: guidelines, statistical analysis, empirical software engineering, programming languages, Bayesian data analysis
Popis: Statistical analysis is the tool of choice to turn data into information and then information into empirical knowledge. However, the process that goes from data to knowledge is long, uncertain, and riddled with pitfalls. To be valid, it should be supported by detailed, rigorous guidelines that help ferret out issues with the data or model and lead to qualified results that strike a reasonable balance between generality and practical relevance. Such guidelines are being developed by statisticians to support the latest techniques for Bayesian data analysis. In this article, we frame these guidelines in a way that is apt to empirical research in software engineering.To demonstrate the guidelines in practice, we apply them to reanalyze a GitHub dataset about code quality in different programming languages. The dataset's original analysis [Ray et al. 55] and a critical reanalysis [Berger et al. 6] have attracted considerable attention-in no small part because they target a topic (the impact of different programming languages) on which strong opinions abound. The goals of our reanalysis are largely orthogonal to this previous work, as we are concerned with demonstrating, on data in an interesting domain, how to build a principled Bayesian data analysis and to showcase its benefits. In the process, we will also shed light on some critical aspects of the analyzed data and of the relationship between programming languages and code quality-such as the impact of project-specific characteristics other than the used programming language.The high-level conclusions of our exercise will be that Bayesian statistical techniques can be applied to analyze software engineering data in a way that is principled, flexible, and leads to convincing results that inform the state-of-The-Art while highlighting the boundaries of its validity. The guidelines can support building solid statistical analyses and connecting their results. Thus, they can help buttress continued progress in empirical software engineering research.
Přístupová URL adresa: https://research.chalmers.se/publication/530678
Databáze: SwePub
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:edsswe&genre=article&issn=1049331X&ISBN=&volume=31&issue=3&date=20220101&spage=&pages=&title=ACM Transactions on Software Engineering and Methodology&atitle=Applying%20Bayesian%20Analysis%20Guidelines%20to%20Empirical%20Software%20Engineering%20Data%3A%20The%20Case%20of%20Programming%20Languages%20and%20Code%20Quality&aulast=Furia%2C%20Carlo%20A&id=DOI:10.1145/3490953
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Furia%20CA
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsswe
DbLabel: SwePub
An: edsswe.oai.research.chalmers.se.59f3f61b.62de.4ddb.a39e.7647684310c7
RelevancyScore: 861
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 861.017700195313
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Furia%2C+Carlo+A%22">Furia, Carlo A</searchLink>, 1979<br /><searchLink fieldCode="AR" term="%22Torkar%2C+Richard%22">Torkar, Richard</searchLink>, 1971<br /><searchLink fieldCode="AR" term="%22Feldt%2C+Robert%22">Feldt, Robert</searchLink>, 1972
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>ACM Transactions on Software Engineering and Methodology</i>. 31(3)
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22guidelines%22">guidelines</searchLink><br /><searchLink fieldCode="DE" term="%22statistical+analysis%22">statistical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22empirical+software+engineering%22">empirical software engineering</searchLink><br /><searchLink fieldCode="DE" term="%22programming+languages%22">programming languages</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+data+analysis%22">Bayesian data analysis</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Statistical analysis is the tool of choice to turn data into information and then information into empirical knowledge. However, the process that goes from data to knowledge is long, uncertain, and riddled with pitfalls. To be valid, it should be supported by detailed, rigorous guidelines that help ferret out issues with the data or model and lead to qualified results that strike a reasonable balance between generality and practical relevance. Such guidelines are being developed by statisticians to support the latest techniques for Bayesian data analysis. In this article, we frame these guidelines in a way that is apt to empirical research in software engineering.To demonstrate the guidelines in practice, we apply them to reanalyze a GitHub dataset about code quality in different programming languages. The dataset's original analysis [Ray et al. 55] and a critical reanalysis [Berger et al. 6] have attracted considerable attention-in no small part because they target a topic (the impact of different programming languages) on which strong opinions abound. The goals of our reanalysis are largely orthogonal to this previous work, as we are concerned with demonstrating, on data in an interesting domain, how to build a principled Bayesian data analysis and to showcase its benefits. In the process, we will also shed light on some critical aspects of the analyzed data and of the relationship between programming languages and code quality-such as the impact of project-specific characteristics other than the used programming language.The high-level conclusions of our exercise will be that Bayesian statistical techniques can be applied to analyze software engineering data in a way that is principled, flexible, and leads to convincing results that inform the state-of-The-Art while highlighting the boundaries of its validity. The guidelines can support building solid statistical analyses and connecting their results. Thus, they can help buttress continued progress in empirical software engineering research.
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/530678" linkWindow="_blank">https://research.chalmers.se/publication/530678</link>
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsswe&AN=edsswe.oai.research.chalmers.se.59f3f61b.62de.4ddb.a39e.7647684310c7
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1145/3490953
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: guidelines
        Type: general
      – SubjectFull: statistical analysis
        Type: general
      – SubjectFull: empirical software engineering
        Type: general
      – SubjectFull: programming languages
        Type: general
      – SubjectFull: Bayesian data analysis
        Type: general
    Titles:
      – TitleFull: Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Furia, Carlo A
      – PersonEntity:
          Name:
            NameFull: Torkar, Richard
      – PersonEntity:
          Name:
            NameFull: Feldt, Robert
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 1049331X
            – Type: issn-print
              Value: 15577392
            – Type: issn-locals
              Value: CTH_SWEPUB
          Numbering:
            – Type: volume
              Value: 31
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: ACM Transactions on Software Engineering and Methodology
              Type: main
ResultId 1