Software development process and project management anti-patterns (AP) are known, reoccurring, bad solutions to common problems in their respective domains. AP descriptions exist almost exclusively in textual form for human consumption, rendering detection in projects difficult at best. Application Lifecycle Management (ALM) tools capture day-to-day reality of software development projects as a byproduct of their use.
Our Software Process Anti-pattern Detector (SPADe) automates the detection of the aforementioned APs, defined in the Software Process Anti-pattern Catalogue (SPAC), in project ALM tool data, extracted by the Project Mining and Storage (ProMiS) tool.
Data from various ALM tools are mined by data pumps and saved in a database with a uniform metamodel [1]. Gathered AP descriptions from literature are condensed in to catalogue entries using a custom description template [2], then operationalized [3] into models over project data and detected by SPADe. Results are shown in the Software Process Anti-pattern Web Interface (SPAWn).
Both data mined, and anti-pattern language constructed from SPAC can also be exported for further analysis in DOT or JSON format, e.g., to Interactive Multimodal Graph Explorer (IMiGEr).
See the ReliSA research group website for more context.
[1] ProMiS Metamodel - P. Pícha and P. Brada, "ALM Tool Data Usage in Software Process Metamodeling," 2016 42th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2016, pp. 1-8, doi: 10.1109/SEAA.2016.37.
[2] Use of ALM data for different analysis - P. Pícha, P. Brada, R. Ramsauer and W. Mauerer, "Towards Architect’s Activity Detection through a Common Model for Project Pattern Analysis," 2017 IEEE International Conference on Software Architecture Workshops (ICSAW), 2017, pp. 175-178, doi: 10.1109/ICSAW.2017.46.
[3] SPAC - P. Brada and P. Picha, "Software process anti-patterns catalogue," 24th European Conference on Pattern Languages of Programs (EuroPLop), 2019, pp. 1–10, doi: 10.1145/3361149.3361178
[4] AP operationalization and detection approach - P. Picha and P. Brada, "Software process anti-pattern detection in project data," 24th European Conference on Pattern Languages of Programs (EuroPLop), 2019, pp. 1–12, doi: 10.1145/3361149.3361169
[5] Use of SPADe data in IMiGEr - L. Holy, P. Picha, R. Lipka and P. Brada, "Software Engineering Projects Analysis using Interactive Multimodal Graph Explorer - IMiGEr," 10th International Conference on Information Visualization Theory and Applications (IVAPP), 2019, pp. 330-337, https://www.scitepress.org/Papers/2019/75798.
[6] AP operationalization through SPEM models - L. Simeckova, P. Brada and P. Picha, "SPEM-Based Process Anti-Pattern Models for Detection in Project Data," 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 2020, pp. 89-92, doi: 10.1109/SEAA51224.2020.00024.
[7] Dataset used for Fire Drill detection case study - S. Hönel, P. Pícha, P. Brada, and L. Rychtarova, "Detection of the Fire Drill anti-pattern: Nine real-world projects with ground truth, issue-tracking data, source code density, models and code," 2021, doi: 10.5281/zenodo.4734053.