View in EDS HTML Full Text PDF Full Text

ARCLIN: automated API mention resolution for unformatted texts.

Saved in:
Bibliographic Details
Title: ARCLIN: automated API mention resolution for unformatted texts.
Authors: Huo, Yintong1 ythuo@cse.cuhk.edu.hk, Su, Yuxin2 suyx35@mail.sysu.edu.cn, Zhang, Hongming3 hzhangal@cse.ust.hk, Lyu, Michael R.1 lyu@cse.cuhk.edu.hk
Source: ICSE: International Conference on Software Engineering. 2022, p138-149. 12p.
Subjects: Stack Overflow (Company), Application program interfaces, Application software, Text mining, Natural language processing
Abstract: Online technical forums (e.g., StackOverflow) are popular platforms for developers to discuss technical problems such as how to use a specific Application Programming Interface (API), how to solve the programming tasks, or how to fix bugs in their code. These discussions can often provide auxiliary knowledge of how to use the software that is not covered by the official documents. The automatic extraction of such knowledge may support a set of downstream tasks like API searching or indexing. However, unlike official documentation written by experts, discussions in open forums are made by regular developers who write in short and informal texts, including spelling errors or abbreviations. There are three major challenges for the accurate APIs recognition and linking mentioned APIs from unstructured natural language documents to an entry in the API repository: (1) distinguishing API mentions from common words; (2) identifying API mentions without a fully qualified name; and (3) disambiguating API mentions with similar method names but in a different library. In this paper, to tackle these challenges, we propose an ARCLIN tool, which can effectively distinguish and link APIs without using human annotations. Specifically, we first design an API recognizer to automatically extract API mentions from natural language sentences by a Conditional Random Field (CRF) on the top of a Bi-directional Long Short-Term Memory (Bi-LSTM) module, then we apply a context-aware scoring mechanism to compute the mention-entry similarity for each entry in an API repository. Compared to previous approaches with heuristic rules, our proposed tool without manual inspection outperforms by 8% in a high-quality dataset Py-mention, which contains 558 mentions and 2,830 sentences from five popular Python libraries. To our best knowledge, ARCLIN is the first approach to achieve full automation of API mention resolution from unformatted text without manually collected labels. [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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 185195809
AccessLevel: 6
PubType: Conference
PubTypeId: conference
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: ARCLIN: automated API mention resolution for unformatted texts.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Huo%2C+Yintong%22">Huo, Yintong</searchLink><relatesTo>1</relatesTo><i> ythuo@cse.cuhk.edu.hk</i><br /><searchLink fieldCode="AR" term="%22Su%2C+Yuxin%22">Su, Yuxin</searchLink><relatesTo>2</relatesTo><i> suyx35@mail.sysu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hongming%22">Zhang, Hongming</searchLink><relatesTo>3</relatesTo><i> hzhangal@cse.ust.hk</i><br /><searchLink fieldCode="AR" term="%22Lyu%2C+Michael+R%2E%22">Lyu, Michael R.</searchLink><relatesTo>1</relatesTo><i> lyu@cse.cuhk.edu.hk</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>. 2022, p138-149. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Stack+Overflow+%28Company%29%22">Stack Overflow (Company)</searchLink><br /><searchLink fieldCode="DE" term="%22Application+program+interfaces%22">Application program interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Application+software%22">Application software</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Online technical forums (e.g., StackOverflow) are popular platforms for developers to discuss technical problems such as how to use a specific Application Programming Interface (API), how to solve the programming tasks, or how to fix bugs in their code. These discussions can often provide auxiliary knowledge of how to use the software that is not covered by the official documents. The automatic extraction of such knowledge may support a set of downstream tasks like API searching or indexing. However, unlike official documentation written by experts, discussions in open forums are made by regular developers who write in short and informal texts, including spelling errors or abbreviations. There are three major challenges for the accurate APIs recognition and linking mentioned APIs from unstructured natural language documents to an entry in the API repository: (1) distinguishing API mentions from common words; (2) identifying API mentions without a fully qualified name; and (3) disambiguating API mentions with similar method names but in a different library. In this paper, to tackle these challenges, we propose an ARCLIN tool, which can effectively distinguish and link APIs without using human annotations. Specifically, we first design an API recognizer to automatically extract API mentions from natural language sentences by a Conditional Random Field (CRF) on the top of a Bi-directional Long Short-Term Memory (Bi-LSTM) module, then we apply a context-aware scoring mechanism to compute the mention-entry similarity for each entry in an API repository. Compared to previous approaches with heuristic rules, our proposed tool without manual inspection outperforms by 8% in a high-quality dataset Py-mention, which contains 558 mentions and 2,830 sentences from five popular Python libraries. To our best knowledge, ARCLIN is the first approach to achieve full automation of API mention resolution from unformatted text without manually collected labels. [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=185195809
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1145/3510003.3510158
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 138
    Subjects:
      – SubjectFull: Stack Overflow (Company)
        Type: general
      – SubjectFull: Application program interfaces
        Type: general
      – SubjectFull: Application software
        Type: general
      – SubjectFull: Text mining
        Type: general
      – SubjectFull: Natural language processing
        Type: general
    Titles:
      – TitleFull: ARCLIN: automated API mention resolution for unformatted texts.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Huo, Yintong
      – PersonEntity:
          Name:
            NameFull: Su, Yuxin
      – PersonEntity:
          Name:
            NameFull: Zhang, Hongming
      – PersonEntity:
          Name:
            NameFull: Lyu, Michael R.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: 2022
              Type: published
              Y: 2022
          Titles:
            – TitleFull: ICSE: International Conference on Software Engineering
              Type: main
ResultId 1