Assessment of issue handling efficiency

Luijten, Bart; Visser, Joost; Zaidman, Andy

doi:10.1109/msr.2010.5463292

Cited by 12 publications

(8 citation statements)

References 4 publications

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“…2 show that most of the values of these metrics are low but there is a long tail of much higher values towards the right, confirming observations from earlier studies [69], [55]. 9. Only the quantile plot for unit complexity is shown, the plots for unit size and unit dependency can be found in [68].…”

Section: Descriptive Statisticssupporting

confidence: 84%

“…Fig. 2 shows box-plots that illustrate the distributions of the system level metrics and a quantile plot that shows the distribution of one of the unit level metrics 9 .…”

Section: Descriptive Statisticsmentioning

confidence: 99%

“…We expect that defect resolution time for a software system is reflected in its associated ITS as previous work suggests [9], [10], [11], [12], [13]. In addition, further indicators of issue handling performance, such as throughput and productivity, can be derived by studying ITS data as shown in [14].…”

mentioning

confidence: 99%

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Test Code Quality and Its Relation to Issue Handling Performance

Athanasiou¹,

Nugroho²,

Visser

et al. 2014

IIEEE Trans. Software Eng.

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Abstract-Automated testing is a basic principle of agile development. Its benefits include early defect detection, defect cause localization and removal of fear to apply changes to the code. Therefore, maintaining high quality test code is essential. This study introduces a model that assesses test code quality by combining source code metrics that reflect three main aspects of test code quality: completeness, effectiveness and maintainability. The model is inspired by the Software Quality Model of the Software Improvement Group which aggregates source code metrics into quality ratings based on benchmarking. To validate the model we assess the relation between test code quality, as measured by the model, and issue handling performance. An experiment is conducted in which the test code quality model is applied to 18 open source systems. The test quality ratings are tested for correlation with issue handling indicators, which are obtained by mining issue repositories. In particular, we study the (1) defect resolution speed, (2) throughput and (3) productivity issue handling metrics. The results reveal a significant positive correlation between test code quality and two out of the three issue handling metrics (throughput and productivity), indicating that good test code quality positively influences issue handling performance.

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Section: Descriptive Statisticssupporting

confidence: 84%

“…Fig. 2 shows box-plots that illustrate the distributions of the system level metrics and a quantile plot that shows the distribution of one of the unit level metrics 9 .…”

Section: Descriptive Statisticsmentioning

confidence: 99%

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Test Code Quality and Its Relation to Issue Handling Performance

Athanasiou¹,

Nugroho²,

Visser

et al. 2014

IIEEE Trans. Software Eng.

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“…Some future directions that we have identified for this line of work are: (i) to apply our analysis to additional (larger) cases to characterize other development methods, (ii) to refine our analysis so that we can discern between artifacts of different testing levels, e.g., unit tests, integration tests, etc., (iii) to automatically filter out shallow changes to code in the Change History View, (iv) to perform a user study with the TeMo tool (similar to Cornelissen et al 2009) (v) to continue investigating statistical techniques to the change history data so that phenomena like test-driven development or phased testing no longer have to be visually recognized (Lubsen et al 2009) and (vi) to continue investigating the relationship between the testing strategy followed and the efficiency of the issue handling process (Luijten et al 2010 Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited. Serge Demeyer is a professor at the University of Antwerp, where he leads the Lab on Reengineering (LORE) part of the Antwerp Systems and software Modelling research group (AnSyMo).…”

Section: Discussionmentioning

confidence: 99%

Studying the co-evolution of production and test code in open source and industrial developer test processes through repository mining

et al. 2010

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Many software production processes advocate rigorous development testing alongside functional code writing, which implies that both test code and production code should co-evolve. To gain insight in the nature of this co-evolution, this paper proposes three views (realized by a tool called TeMo) that combine information from a software project's versioning system, the size of the various artifacts and the test coverage reports. We validate these views against two open source and one industrial software project and evaluate our results both with the help of log messages, code inspections and the original developers of the software system. With these views we could recognize different co-evolution scenarios (i.e., synchronous and phased) and make relevant observations for both developers as well as test engineers.

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“…Several studies have focused on GNOME's bug database (Bugzilla), whose purpose have been: (1) predict the bug severity [40,42] (2) determine quality of bug reports [2,69], and (3) determine the efficiency of developers to address issues [44].…”

Section: Previous Studies Of Gnomementioning

confidence: 99%

Herding cats in a FOSS ecosystem: a tale of communication and coordination for release management

Poo-Caamaño

Knauss

Singer

et al. 2017

J Internet Serv Appl

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Release management in large-scale software development projects requires significant communication and coordination. It is particularly challenging in Free and Open Source Software (FOSS) ecosystems, in which hundreds of loosely connected developers and their projects are coordinated to release software to a schedule. To better understand this process and its challenges, we analyzed over two and half years of communication in the GNOME ecosystem and studied developers' interactions. Through a case study, we cataloged communication channels, determined the main channel from which we categorized high level communication and coordination activities spanning five releases, and triangulated our results by interviewing ten key developers. We found that a release schedule, influence (instead of direct control), and diversity are the main factors that positively impact the release process in the GNOME ecosystem. We report a set of lessons learned that encapsulates our understanding of how the Release Management process function in a FOSS ecosystem, we learned that: (1) ensure that the release team follows the main communication channels used by developers, (2) provide a common place for coordination for an ecosystem, (3) consider including both good technical and social skills in a release team, (4) aim for a diverse release team, (5) based on lack of power, lobbying and consensus based management must be followed, (6) help the release team in the coordination process with a well defined schedule, and (7) release team work is different from regular software work. Our results can help organizations build better large-scale teams and show that research focused on individual projects might miss important parts of the picture.

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Assessment of issue handling efficiency

Cited by 12 publications

References 4 publications

Test Code Quality and Its Relation to Issue Handling Performance

Test Code Quality and Its Relation to Issue Handling Performance

Studying the co-evolution of production and test code in open source and industrial developer test processes through repository mining

Herding cats in a FOSS ecosystem: a tale of communication and coordination for release management

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