NRFixer: sentiment based model for predictingthe fixability of non-reproducible bugs

Anjali Goyal; Neetu Sardana

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Title	NRFixer: sentiment based model for predictingthe fixability of non-reproducible bugs
Persons	Authors: Anjali Goyal, Neetu Sardana Partner: Wrocław University of Science and Technology
Description	Software maintenance is an essential step in software development life cycle. Nowadays, softwarecompanies spend approximately 45% of total cost in maintenance activities. Large software projects maintain bug repositories to collect, organize and resolve bug reports. Sometimes it is difficult to reproduce the reported bug with the information present in a bug report and thus this bug is marked with resolution non-reproducible (NR). When NR bugs are reconsidered, a few of them might get fixed (NR-to-fix) leaving the others with the same resolution (NR). To analyse the behaviour of developers towards NR-to-fix and NR bugs, the sentiment analysis of NR bug report textual contents has been conducted. The sentiment analysis of bug reports shows that NR bugs’sentiments incline towards more negativity than reproducible bugs. Also, there is a noticeable opinion drift found in the sentiments of NR-to-fix bug reports. Observations driven from this analysis were an inspiration to develop a model that can judge the fixability of NR bugs. Thusa framework, NRFixer, which predicts the probability of NR bug fixation, is proposed. NRFixer wasevaluated with two dimensions. The first dimension considers meta-fields of bug reports (model-1) and the other dimension additionally incorporates the sentiments (model-2) of developers forprediction. Both models were compared using various machine learning classifiers (Zero-R, naiveBayes, J48, random tree and random forest). The bug reports of Firefox and Eclipse projects were used to test NRFixer. In Firefox and Eclipse projects, J48 and Naive Bayes classifiers achieve the best prediction accuracy, respectively. It was observed that the inclusion of sentiments inthe prediction model shows a rise in the prediction accuracy ranging from 2 to 5% for various classifiers. (English) Description in another language: Zasób o zwiększonej dostępności dla ON (osób z niepełnosprawnościami). (Polish)
Keywords	"adaptacja"@pl, "informatyka"@pl
Classification	Resource type: article, chapter Scientific discipline: dziedzina nauk technicznych / informatyka (2011) Destination group: general public, students, scientists Harmful content: No
Characteristics	Title of source document: e-Informatica: Software Engineering Journal Numbering: Vol. 11, issue 1 Place of publication: Wrocław Publisher: Oficyna Wydawnicza Politechniki Wrocławskiej Time of publication: 2017 From page: 109 To page: 120 ISSN: 1897-7979 Resource language: English Identifiers: DOI: 10.5277/e-Inf170105
External links	e-Informatica Software Engineering Journal, Vol. 11, 2017
License	ID-NC-ND
Technical information	Submitter: Magdalena Kruczek Availability date: 29-08-2019
Collections	Kolekcja Politechniki Wrocławskiej, Kolekcja e-Dostępność

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Anjali Goyal, Neetu Sardana. NRFixer: sentiment based model for predictingthe fixability of non-reproducible bugs. [article, chapter] Available in Atlas of Open Science Resources, . License: ID-NC-ND, https://azon.e-science.pl/licencje/ID-NC-ND_PWr.pdf. Date of access: DD.MM.RRRR.

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