Racing Against the Clock: Exploring the Impact of Scheduled Deadlines on Technical Debt
Bibliographic record
Abstract
Background: Technical Debt (TD) describes suboptimal software development practices with long-term consequences, such as defects and vulnerabilities. Deadlines are a leading cause of the emergence of TD in software systems. While multiple aspects of TD have been studied, the empirical research findings on the impact of deadlines are still inconclusive. Aims: This study investigates the impact of scheduled deadlines on TD. It analyzes how scheduled deadlines affect code quality, commit activities, and issues in issue-tracking systems. Method: We analyzed eight Open Source Software (OSS) projects with regular release schedules using SonarQube. We analyzed 12.3k commits and 371 releases across these eight OSS projects. The study combined quantitative metrics with qualitative analyses to comprehensively understand TD accumulation under scheduled deadlines. Results: Our findings indicated that some projects had a clear increase in TD as deadlines approached (with above 50% of releases having increasing TD accumulation as deadlines approached), while others managed to maintain roughly the same amount of TD. Analysis of commit activities and issue tracking revealed that deadline proximity could lead to increased commit frequency and bug-related issue creation. Conclusions: Our study highlights that, in some cases, impending deadlines have a clear impact on TD. The findings pinpoint the need to mitigate last-minute coding rushes and the risks associated with deadline-driven TD accumulation.
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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 | 0.012 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".