Developer load balancing bug triage: Developed load balance

Yadav, Asmita; Baljon, Mohammed; Mishra, Shailendra; Singh, Sandeep Kumar; Saxena, Sharad; Sharma, Sunil Kumar

doi:10.1111/exsy.13006

Cited by 4 publications

(1 citation statement)

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“…Then, the framework uses integer programming to assign the bugs to developers according to the schedules of the developers and the dependencies of the bugs. Yadav et al [23] propose a framework named Developer Load Balance (DLB) model to assign bugs to developers based on the following criteria: the current workload, work experience, and technical expertise of the developers. A ranking list of developers is generated for each of these criteria.…”

Section: Related Workmentioning

confidence: 99%

Bug Report Classification with Ensemble Learning for Closed-Source Software

Yılmaz,

Öztürk,

Köksal

2023

Preprint

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This paper introduces a set of datasets grouped under the name Turkish Software Bug Reports (TSBR), which comprises commercial software bug reports from a closed-source project. We investigate and report the statistical properties and classification difficulty of the TSBR datasets. We employ various methods from the text classification literature to apply several classification tasks related to software development on the TSBR datasets. The methods we employ include traditional machine learning (ML) methods such as k-nearest neighbors (KNN) and random forest (RF); sequential deep learning (DL) models such as gated recurrent unit (GRU) and convolutional neural network (CNN); transformer-based language models; and ensembles of the employed models. Our work is among the first efforts in automated bug report classification literature that uses ensembles of DL models.

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Section: Related Workmentioning

confidence: 99%