On the Prevalence, Co-occurrence, and Impact of Infrastructure-as-Code Smells
Bibliographic record
Abstract
In modern software systems, Infrastructure-as-Code (IaC) tools play a pivotal role in automating the management of various infrastructure resources such as networks, databases, and services. This automation is done through code-based specification files, commonly known as IaC files. Similarly to other code files, IaC files can suffer from violations of established implementation and design standards, i.e., IaC smells. Although prior research has studied various aspects of traditional smells in non-IaC artifacts, there is little knowledge of how IaC smells are prevalent, co-occurring, and impacting the change and defect proneness of IaC code. To fill this gap, we conduct an empirical study encompassing 82 Puppet-based open-source projects. Our investigation focused on 12 types of IaC smells in both implementation and design levels. Our findings reveal that IaC smells do not manifest uniformly, as IaC smells that are particularly associated with modularity issues, exhibit high prevalence rates across projects. Additionally, we found that 74% of IaC files are smelly and over 52% of the smelly IaC files have at least two co-occurring IaC smells. Furthermore, our findings highlight that, on average, smelly IaC files are modified nearly 3.8 times, in terms of number of commits, more frequently than non-smelly IaC files. Furthermore, smelly IaC files are found to be 3.1 times more prone to larger code changes, in terms of code churn, than non-smelly IaC files. Additionally, we found that smelly IaC files are 3.3 times more prone to the introduction of defects that are likely to persist in 1.65 more commits before being fixed than non-smelly IaC files. These findings advocate developers to be more aware of IaC smells in their projects and consider their correction.
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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 | 0.015 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".