The More the Merrier: Leveraging on the Bug Inflow to Guide Software Maintenance
Författare
Summary, in English
Issue management, a central part of software maintenance, requires much effort for complex software systems. The continuous inflow of issue reports makes it hard for developers to stay on top of the
situation, and the threatening information overload makes activities such as duplicate management, Issue Assignment (IA), and Change Impact Analysis (CIA) tedious and error-prone. Still, most
practitioners work with tools that act as little more than issue containers.
Machine Learning encompasses approaches that identify patterns or make predictions based on empirical data. While humans have limited ability to work with big data, ML instead tends to
improve the more training data that is available. Consequently, we argue that the challenge of information overload in issue management appears to be particularly suitable for ML-based tool support. While others have initially explored the area, we develop two ML-based tools, and evaluate them in proprietary software engineering contexts.
We replicated [1] for five projects in two companies, and our
automated IA obtains an accuracy matching the current manual processes. Thus, as our solution delivers instantaneous IA, an organization can potentially save considerable analysis effort. Moreover, for the most comprehensive of the five projects, we implemented automated CIA in the tool ImpRec [3]. We evaluated the tool in a longitudinal in situ study, i.e., deployment in two development teams in industry. Based on log analysis and complementary interviews using the QUPER model [2] for utility assessment, we conclude that ImpRec offered helpful support in the CIA task.
situation, and the threatening information overload makes activities such as duplicate management, Issue Assignment (IA), and Change Impact Analysis (CIA) tedious and error-prone. Still, most
practitioners work with tools that act as little more than issue containers.
Machine Learning encompasses approaches that identify patterns or make predictions based on empirical data. While humans have limited ability to work with big data, ML instead tends to
improve the more training data that is available. Consequently, we argue that the challenge of information overload in issue management appears to be particularly suitable for ML-based tool support. While others have initially explored the area, we develop two ML-based tools, and evaluate them in proprietary software engineering contexts.
We replicated [1] for five projects in two companies, and our
automated IA obtains an accuracy matching the current manual processes. Thus, as our solution delivers instantaneous IA, an organization can potentially save considerable analysis effort. Moreover, for the most comprehensive of the five projects, we implemented automated CIA in the tool ImpRec [3]. We evaluated the tool in a longitudinal in situ study, i.e., deployment in two development teams in industry. Based on log analysis and complementary interviews using the QUPER model [2] for utility assessment, we conclude that ImpRec offered helpful support in the CIA task.
Avdelning/ar
Publiceringsår
2015
Språk
Engelska
Publikation/Tidskrift/Serie
Tiny Transactions on Computer Science
Volym
3
Fulltext
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Dokumenttyp
Artikel i tidskrift
Ämne
- Computer Science
Status
Published
Projekt
- Embedded Applications Software Engineering