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An NLP approach for Identification and Detection of Self-admitted Technical Debt: A Review of existing Techniques

by Adelaide Anim-Annor, Fredrick Boafo, Solomon Mensah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 31
Year of Publication: 2023
Authors: Adelaide Anim-Annor, Fredrick Boafo, Solomon Mensah
10.5120/ijca2023923074

Adelaide Anim-Annor, Fredrick Boafo, Solomon Mensah . An NLP approach for Identification and Detection of Self-admitted Technical Debt: A Review of existing Techniques. International Journal of Computer Applications. 185, 31 ( Aug 2023), 38-44. DOI=10.5120/ijca2023923074

@article{ 10.5120/ijca2023923074,
author = { Adelaide Anim-Annor, Fredrick Boafo, Solomon Mensah },
title = { An NLP approach for Identification and Detection of Self-admitted Technical Debt: A Review of existing Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2023 },
volume = { 185 },
number = { 31 },
month = { Aug },
year = { 2023 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number31/32895-2023923074/ },
doi = { 10.5120/ijca2023923074 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:35.738718+05:30
%A Adelaide Anim-Annor
%A Fredrick Boafo
%A Solomon Mensah
%T An NLP approach for Identification and Detection of Self-admitted Technical Debt: A Review of existing Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 31
%P 38-44
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Programmers tend to leave deficient, non-permanent bypass and demented codes that require rework in software development and such phenomenon is referred to as Self-admitted Technical Debt (SATD). Previous studies have shown that SATD is common in released software artefacts and is mostly found in source code comments. SATD negatively affects software project development and incurs high maintenance overheads. In this study, the authors seek to identify plausible approaches utilized by researchers to identify and detect SATD prone tasks in software artefacts prior to release to the market or clients. Accordingly, a literature review is carried out to perform this investigative study. Two popular approaches were found for identifying and detecting SATD prone tasks from a pool of SATD related research papers, namely manual and text mining approach.

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Index Terms

Computer Science
Information Sciences

Keywords

Self-admitted Technical Debt Textual indicators Source code comment Lines of Code Text Mining

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