Design patterns impact on software quality: Where are the theories?
Bibliographic record
Abstract
Software engineers are creators of habits. During software development, they follow again and again the same patterns when architecting, designing and implementing programs. Alexander introduced such patterns in architecture in 1974 and, 20 years later, they made their way in software development thanks to the work of Gamma et al. Software design patterns were promoted to make the design of programs more "flexible, modular, reusable, and understandable". However, ten years later, these patterns, their roles, and their impact on software quality were not fully understood. We then set out to study the impact of design patterns on different quality attributes and published a paper entitled "Do Design Patterns Impact Software Quality Positively?" in the proceedings of the 12 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> European Conference on Software Maintenance and Reengineering (CSMR) in 2008. Ten years later, this paper received the Most Influential Paper award at the 25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> International Conference on Software Analysis, Evolution, and Reengineering (SANER) in 2018. In this retrospective paper for the award, we report and reflect on our and others' studies on the impact of design patterns, discussing some key findings reported about design patterns. We also take a step back from these studies and re-examine the role that design patterns should play in software development. Finally, we outline some avenues for future research work on design patterns, e.g., the identification of the patterns really used by developers, the theories explaining the impact of patterns, or their use to raise the abstraction level of programming languages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".