Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-18599
Title: Exploiting natural language structures in software informal documentation
Authors : Di Sorbo, Andrea
Panichella, Sebastiano
Visaggio, Corrado Aaron
Di Penta, Massimiliano
Canfora, Gerardo
Gall, Harald C.
et. al : No
Published in : IEEE Transactions on Software Engineering
Publisher / Ed. Institution : IEEE
Issue Date: 2019
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (publication)
Language : English
Subject (DDC) : 005: Computer programming, programs and data
Abstract: Communication means, such as issue trackers, mailing lists, Q&A forums, and app reviews, are premier means of collaboration among developers, and between developers and end-users. Analyzing such sources of information is crucial to build recommenders for developers, for example suggesting experts, re-documenting source code, or transforming user feedback in maintenance and evolution strategies for developers. To ease this analysis, in previous work we proposed DECA (Development Emails Content Analyzer), a tool based on Natural Language Parsing that classifies with high precision development emails' fragments according to their purpose. However, DECA has to be trained through a manual tagging of relevant patterns, which is often effort-intensive, error-prone and requires specific expertise in natural language parsing. In this paper, we first show, with a study involving Master's and Ph.D. students, the extent to which producing rules for identifying such patterns requires effort, depending on the nature and complexity of patterns. Then, we propose an approach, named NEON (Nlp-based softwarE dOcumentation aNalyzer), that automatically mines such rules, minimizing the manual effort. We assess the performances of NEON in the analysis and classification of mobile app reviews, developers discussions, and issues. NEON simplifies the patterns' identification and rules' definition processes, allowing a savings of more than 70% of the time otherwise spent on performing such activities manually. Results also show that NEON-generated rules are close to the manually identified ones, achieving comparable recall.
Further description : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Article in scientific journal
DOI : 10.1109/TSE.2019.2930519
10.21256/zhaw-18599
ISSN: 0098-5589
1939-3520
URI: https://digitalcollection.zhaw.ch/handle/11475/18599
Appears in Collections:Publikationen School of Engineering

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