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Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL
Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N
Bug Fix Time Optimization Using Matrix Factorization and Iterative Gale-Shaply Algorithms
Bug triage is an essential task in software maintenance phase. It assigns
developers (fixers) to bug reports to fix them. This process is performed
manually by a triager, who analyzes developers profiles and submitted bug
reports to make suitable assignments. Bug triaging process is time consuming
thus automating this process is essential to improve the quality of software.
Previous work addressed triaging problem either as an information retrieval or
classification problem. This paper tackles this problem as a resource
allocation problem, that aims at the best assignments of developers to bug
reports, that reduces the total fixing time of the newly submitted bug reports,
in addition to the even distribution of bug reports over developers. In this
paper, a combination of matrix factorization and Gale Shapely algorithm,
supported by the differential evolution is firstly introduced to optimize the
total fix time and normalize developers work load. Matrix factorization is used
to establish a recommendation system for Gale-Shapley to make assignment
decisions. Differential evolution provides the best set of weights to build
developers score profiles. The proposed approach is assessed over three
repositories, Linux, Apache and Eclipse. Experimental results show that the
proposed approach reduces the bug fixing time, in comparison to the manual
triage, by 80.67%, 23.61% and 60.22% over Linux, Eclipse and Apache
respectively. Moreover, the workload for the developers is uniform.Comment: 14 page, 7 figures, 8 tables, 10 equation
Reason Maintenance - Conceptual Framework
This paper describes the conceptual framework for reason maintenance developed as part of
WP2
ADPTriage: Approximate Dynamic Programming for Bug Triage
Bug triaging is a critical task in any software development project. It
entails triagers going over a list of open bugs, deciding whether each is
required to be addressed, and, if so, which developer should fix it. However,
the manual bug assignment in issue tracking systems (ITS) offers only a limited
solution and might easily fail when triagers must handle a large number of bug
reports. During the automated assignment, there are multiple sources of
uncertainties in the ITS, which should be addressed meticulously. In this
study, we develop a Markov decision process (MDP) model for an online bug
triage task. In addition to an optimization-based myopic technique, we provide
an ADP-based bug triage solution, called ADPTriage, which has the ability to
reflect the downstream uncertainty in the bug arrivals and developers'
timetables. Specifically, without placing any limits on the underlying
stochastic process, this technique enables real-time decision-making on bug
assignments while taking into consideration developers' expertise, bug type,
and bug fixing time. Our result shows a significant improvement over the myopic
approach in terms of assignment accuracy and fixing time. We also demonstrate
the empirical convergence of the model and conduct sensitivity analysis with
various model parameters. Accordingly, this work constitutes a significant step
forward in addressing the uncertainty in bug triage solution
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