Joint Autoregressive and Graph Models for Software and Developer Social Networks

Hazra, Rima; Aggarwal, Hardik; Goyal, Pawan; Mukherjee, Animesh; Chakrabarti, Soumen

doi:10.1007/978-3-030-72113-8_15

Cited by 3 publications

(1 citation statement)

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“…There is very little research on the utility of graphs in the software domain to solve major software system problems. In [12], the authors studied in detail the problem of bug urgency ranking and developer recommendation with the help of underlying graph structures. The authors curated a dataset from the Ubuntu platform for their experiments and developed various machine learning models for the ranking and recommendation problems.…”

Section: Related Workmentioning

confidence: 99%

Is this bug severe? A text-cum-graph based model for bug severity prediction

Hazra,

Dwivedi,

Mukherjee

2021

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Repositories of large software systems have become commonplace. This massive expansion has resulted in the emergence of various problems in these software platforms including identification of (i) bugprone packages, (ii) critical bugs, and (iii) severity of bugs. One of the important goals would be to mine these bugs and recommend them to the developers to resolve them. The first step to this is that one has to accurately detect the extent of severity of the bugs. In this paper, we take up this task of predicting the severity of bugs in the near future. Contextualized neural models built on the text description of a bug and the user comments about the bug help to achieve reasonably good performance. Further information on how the bugs are related to each other in terms of the ways they affect packages can be summarised in the form of a graph and used along with the text to get additional benefits.

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Section: Related Workmentioning

confidence: 99%