Do Code Smells Impact the Effort of Different Maintenance Programming Activities?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Empirical studies have shown so far that code smells have relatively low impact over maintenance effort at file level. We surmise that previous studies have found low effects of code smells because the effort considered is a "sheer-effort" that does not distinguish between the kinds of developers' activities. In our study, we investigate the effects of code smells at the activity level. Examples of activities are: reading, editing, searching, and navigating, which are performed independently over different files during maintenance. We conjecture that structural attributes represented in the form of different code smells do indeed have an effect on the effort for performing certain kinds of activities. To verify this conjecture, we revisit a previous study about the impact of code smell on maintenance effort, using the same dataset, but considering activity effort. Six professional developers were hired to perform three maintenance tasks on four functionally equivalent Java Systems. Each developer performs two maintenance tasks. During maintenance task, we monitor developers' logs. Then, we define an annotation schema to identify developers' activities and assess whether code smells affect different maintenance activities. Results show that different code smells affect differently activity effort. Yet, the size of the changes performed to solve the task impacts the effort of all activities more than code smells and file size. While code smells impact the editing and navigating effort more than file size, the file size impacts the reading and searching activities more than code smells. One major implication of these results is that if code smells indeed affect the effort of certain kinds of activities, it means that their effects are contingent on the type of maintenance task at hand, where some kinds of activities will become more predominant than others.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it