Coping with Inconsistent Models of Requirements

https://confws19.hitec-hamburg.de/Peer reviewe

Improved management of issue dependencies in issue trackers of large collaborative projects

Issue trackers, such as Jira, have become the prevalent collaborative tools in software engineering for managing issues, such as requirements, development tasks, and software bugs. However, issue trackers inherently focus on the lifecycle of single issues, although issues have and express dependencies on other issues that constitute issue dependency networks in large complex collaborative projects. The objective of this study is to develop supportive solutions for the improved management of dependent issues in an issue tracker. This study follows the Design Science methodology, consisting of eliciting drawbacks and constructing and evaluating a solution and system. The study was carried out in the context of The Qt Company's Jira, which exemplifies an actively used, almost two-decade-old issue tracker with over 100,000 issues. The drawbacks capture how users operate with issue trackers to handle issue information in large, collaborative, and long-lived projects. The basis of the solution is to keep issues and dependencies as separate objects and automatically construct an issue graph. Dependency detections complement the issue graph by proposing missing dependencies, while consistency checks and diagnoses identify conflicting issue priorities and release assignments. Jira's plugin and service-based system architecture realize the functional and quality concerns of the system implementation. We show how to adopt the intelligent supporting techniques of an issue tracker in a complex use context and a large data-set. The solution considers an integrated and holistic system view, practical applicability and utility, and the practical characteristics of issue data, such as inherent incompleteness.The work presented in this paper has been conducted within the scope of the Horizon 2020 project OpenReq, which is supported by the European Union under Grant Nr. 732463. We are grateful for the provision of the Finnish computing infrastructure to carry out the tests (persistent identifier urn:nbn:fi:research-infras-2016072533). This paper has been funded by the Spanish Ministerio de Ciencia e Innovacionúnder project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Automated Detection of Typed Links in Issue Trackers

Stakeholders in software projects use issue trackers like JIRA to capture and manage issues, including requirements 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 15 JIRA repositories, we study how well state-of-the-art machine learning models can automatically detect common link types. We found that a pure BERT model trained on titles and descriptions of linked issues significantly outperforms other optimized deep learning models, achieving an encouraging average macro F1-score of 0.64 for detecting 9 popular link types across all repositories (weighted F1-score of 0.73). For the specific Subtask- and Epic- links, the model achieved top F1-scores of 0.89 and 0.97, respectively. Our model does not simply learn the textual similarity of the issues. In general, shorter issue text seems to improve the prediction accuracy with a strong negative correlation of -0.70. 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. We also observed significant differences across the repositories, depending on how they are used and by whom.Comment: Accepted at RE2022, eCF Paper Id: 165514626434

Improved management of issue dependencies in issue trackers of large collaborative projects

Issue trackers, such as Jira, have become the prevalent collaborative tools in software engineering for managing issues, such as requirements, development tasks, and software bugs. However, issue trackers inherently focus on the lifecycle of single issues, although issues have and express dependencies on other issues that constitute issue dependency networks in large complex collaborative projects. The objective of this study is to develop supportive solutions for the improved management of dependent issues in an issue tracker. This study follows the Design Science methodology, consisting of eliciting drawbacks and constructing and evaluating a solution and system. The study was carried out in the context of The Qt Company's Jira, which exemplifies an actively used, almost two-decade-old issue tracker with over 100,000 issues. The drawbacks capture how users operate with issue trackers to handle issue information in large, collaborative, and long-lived projects. The basis of the solution is to keep issues and dependencies as separate objects and automatically construct an issue graph. Dependency detections complement the issue graph by proposing missing dependencies, while consistency checks and diagnoses identify conflicting issue priorities and release assignments. Jira's plugin and service-based system architecture realize the functional and quality concerns of the system implementation. We show how to adopt the intelligent supporting techniques of an issue tracker in a complex use context and a large data-set. The solution considers an integrated and holistic system view, practical applicability and utility, and the practical characteristics of issue data, such as inherent incompleteness.Peer reviewe

Structural, optical, and electrical properties of TiO2 thin films deposited by ALD: Impact of the substrate, the deposited thickness and the deposition temperature

International audienceTiO2 films were deposited by ALD on Si and glass substrates. FTIR analysis reveals an incomplete process for deposition temperatures below 160°C. The transition from the amorphous to the crystallized anatase phase is observed during the variation of the deposition temperature. Films were uniform and homogeneous, with a crystallization threshold temperature depending on the substrate’s nature. This delay in crystallization temperature was highlighted by many characterization techniques and found higher by about 50°C on glass compared to Si substrate. We have also identified the determining role of the deposition temperature and the thickness in the crystallization process and we propose a growth model, independently of the substrate’s nature, using different structural analyses. TiO2 refractive index (n) and extinction coefficient (k) were studied at various deposition temperature. The evolution of the TiO2 polarizability (αopt) with material density was determined from n values, showing a large variation of polarizability as a function of material density, in agreement and complementary with other studies. The investigation of the dielectric properties at low frequency shows that the losses and relaxation in TiO2 decrease with deposition temperature, reaching at 300°C a high and frequency-independent dielectric constant, close to the one reported for polycrystalline anatase