Traceability Network Analysis: A Case Study of Links in Issue Tracking Systems

Nicholson, Alexander; Arya, Deeksha M.; Guo, Jin

doi:10.1109/aire51212.2020.00013

Cited by 11 publications

(13 citation statements)

References 35 publications

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“…Our categorization scheme and the overall prevalence and complementary usage across the repositories can provide a concrete guidance for practitioners. Issues with many links or multiple link types might also indicate that an issue is too big and need to be broken down into multiple parts [29].…”

Section: Implications For Practitionersmentioning

confidence: 99%

Beyond Duplicates: Towards Understanding and Predicting Link Types in Issue Tracking Systems

Lüders,

Bouraffa,

Maalej

2022

Preprint

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Software projects use Issue Tracking Systems (ITS) like JIRA to track issues and organize the workflows around them. Issues are often inter-connected via different links such as the default JIRA link types Duplicate, Relate, Block, or Subtask. While previous research has mostly focused on analyzing and predicting duplication links, this work aims at understanding the various other link types, their prevalence, and characteristics towards a more reliable link type prediction. For this, we studied 607,208 links connecting 698,790 issues in 15 public JIRA repositories. Besides the default types, the custom types Depend, Incorporate, Split, and Cause were also common. We manually grouped all 75 link types used in the repositories into five general categories: General Relation, Duplication, Composition, Temporal / Causal, and Workflow. Comparing the structures of the corresponding graphs, we observed several trends. For instance, Duplication links tend to represent simpler issue graphs often with two components and Composition links present the highest amount of hierarchical tree structures (97.7%). Surprisingly, General Relation links have a significantly higher transitivity score than Duplication and Temporal / Causal links.Motivated by the differences between the link types and by their popularity, we evaluated the robustness of two state-of-theart duplicate detection approaches from the literature on the JIRA dataset. We found that current deep-learning approaches confuse between Duplication and other links in almost all repositories. On average, the classification accuracy dropped by 6% for one approach and 12% for the other. Extending the training sets with other link types seems to partly solve this issue. We discuss our findings and their implications for research and practice.

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Section: Implications For Practitionersmentioning

confidence: 99%

Beyond Duplicates: Towards Understanding and Predicting Link Types in Issue Tracking Systems

Lüders,

Bouraffa,

Maalej

2022

Preprint

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“…ITSs resemble change requests or just-in-time requirements that are triaged and addressed by team members continually [13]. As a result, traceability for ITSs has attracted some attention in recent literature [33], [42]. The relationship between on particular issue has been investigated in previous work.…”

Section: Trace Links In Issue Tracking Systemsmentioning

confidence: 99%

“…Our study is based on a dataset created by Nicholson et. al. for their empirical study of Jira issue networks [32], [33]. The complete dataset was mined from the sixty-six Apache Open Source projects and contains a total of 71, 087 trace links Fig.…”

Section: A Issue Links and Their Labels For Apache Projectsmentioning

confidence: 99%

“…On average, more than 18% of issues in a project have links to other issues with a standard deviation of 10%. In terms of the link labels used in practice, previous work dataset suggests that the distribution of the occurrence of different labels is highly skewed [33] -among 16 unique link labels, related to label accounts for nearly half of the links (45.56%). Furthermore, a non-trivial portion of link label categories falls below the 1% of the trace links.…”

Section: A Issue Links and Their Labels For Apache Projectsmentioning

confidence: 99%

“…Making use of the link label functionality in the ITSs requires additional effort to create and maintain these labels [33]. Currently, project stakeholders can only assign and update the labels manually.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Issue Link Label Recovery and Prediction for Open Source Software

Nicholson¹,

Guo²

2021

Preprint

Self Cite

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Modern open source software development heavily relies on the issue tracking systems to manage their feature requests, bug reports, tasks, and other similar artifacts. Together, those "issues" form a complex network with links to each other. The heterogeneous character of issues inherently results in varied link types and therefore poses a great challenge for users to create and maintain the label of the link manually. The goal of most existing automated issue link construction techniques ceases with only examining the existence of links between issues. In this work, we focus on the next important question of whether we can assess the type of issue link automatically through a data-driven method. We analyze the links between issues and their labels used the issue tracking system for 66 open source projects. Using three projects, we demonstrate promising results when using supervised machine learning classification for the task of link label recovery with careful model selection and tuning, achieving F1 scores of between 0.56-0.70 for the three studied projects. Further, the performance of our method for future link label prediction is convincing when there is sufficient historical data. Our work signifies the first step in systematically manage and maintain issue links faced in practice.

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On understanding and predicting issue links

Lüders,

Pietz,

Maalej

2023

Requirements Eng

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Stakeholders in software projects use issue trackers like JIRA or Bugzilla to capture and manage issues, including requirements, feature requests, and bugs. To ease issue navigation and structure project knowledge, stakeholders manually connect issues via links of certain types that reflect different dependencies, such as Epic-, Block-, Duplicate-, or Relate- links. Based on a large dataset of 16 JIRA repositories, we study the commonalities and differences in linking practices and link types across the repositories. We then investigate how state-of-the-art machine learning models can predict common link types. We observed significant differences across the repositories and link types, depending on how they are used and by whom. Additionally, we observed several inconsistencies, e.g., in how Duplicate links are used. We found that a transformer model trained on titles and descriptions of linked issues significantly outperforms other optimized models, achieving an encouraging average macro F1-score of 0.64 for predicting nine popular link types across all repositories (weighted F1-score of 0.73). For the specific Subtask- and Epic- links, the model achieves top F1-scores of 0.89 and 0.97, respectively. If we restrict the task to predict the mere existence of links, the average macro F1-score goes up to 0.95. In general, the shorter issue text, possibly indicating precise issues, seems to improve the prediction accuracy with a strong negative correlation of $$-$$ - 0.73. We found that Relate-links often get confused with the other links, which suggests that they are likely used as default links in unclear cases. Our findings particularly on the quality and heterogeinity of issue link data have implications for researching and applying issue link prediction in practice.

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Traceability Network Analysis: A Case Study of Links in Issue Tracking Systems

Cited by 11 publications

References 35 publications

Beyond Duplicates: Towards Understanding and Predicting Link Types in Issue Tracking Systems

Beyond Duplicates: Towards Understanding and Predicting Link Types in Issue Tracking Systems

Issue Link Label Recovery and Prediction for Open Source Software

On understanding and predicting issue links

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