Are Multi-Language Design Smells Fault-Prone? An Empirical Study
Bibliographic record
Abstract
Nowadays, modern applications are developed using components written in different programming languages and technologies. The cost benefits of reuse and the advantages of each programming language are two main incentives behind the proliferation of such systems. However, as the number of languages increases, so do the challenges related to the development and maintenance of these systems. In such situations, developers may introduce design smells (i.e., anti-patterns and code smells) which are symptoms of poor design and implementation choices. Design smells are defined as poor design and coding choices that can negatively impact the quality of a software program despite satisfying functional requirements. Studies on mono-language systems suggest that the presence of design smells may indicate a higher risk of future bugs and affects code comprehension, thus making systems harder to maintain. However, the impact of multi-language design smells on software quality such as fault-proneness is yet to be investigated. In this article, we present an approach to detect multi-language design smells in the context of JNI systems. We then investigate the prevalence of those design smells and their impacts on fault-proneness. Specifically, we detect 15 design smells in 98 releases of 9 open-source JNI projects. Our results show that the design smells are prevalent in the selected projects and persist throughout the releases of the systems. We observe that, in the analyzed systems, 33.95% of the files involving communications between Java and C/C++ contain occurrences of multi-language design smells. Some kinds of smells are more prevalent than others, e.g., Unused Parameters , Too Much Scattering , and Unused Method Declaration . Our results suggest that files with multi-language design smells can often be more associated with bugs than files without these smells, and that specific smells are more correlated to fault-proneness than others. From analyzing fault-inducing commit messages, we also extracted activities that are more likely to introduce bugs in smelly files. We believe that our findings are important for practitioners as it can help them prioritize design smells during the maintenance of multi-language systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".