Towards a Theory of Technical Debt Ownership: An Exploratory Field Study
Bibliographic record
Abstract
In recent years, Technical Debt (TD) has received a significant amount of attention from research and practice due to its critical impacts on the software industry. Along similar lines as financial debt, the metaphor of TD explains the consequences of shortcuts taken or sub-optimal decisions made during the systems development lifecycle to speed up time to market and reduce development costs in the short-term. However, in the long-term, since TD has a negative influence on system quality, it may lead to a significant amount of extra maintenance costs. Despite its significant importance, especially with regard to information systems development (ISD), TD has been almost completely neglected by IS research. In addition, even in the software engineering discipline where TD has received a considerable amount of attention, there is a lack of research on TD ownership to explain who must be accountable for taking on TD. As a first step to address this gap, we conducted an exploratory field study and collected interview and survey data from software professionals active across industrial domains. Using the Accountability Theory as a lens, we first confirmed the relationships among perceived accountability and occurrence of TD. Next, we performed Qualitative Comparative Analysis (QCA) to explore the presence and absence of factors leading to perceived accountability. Our study extends the accountability theory by applying it in an ISD context to examine TD ownership. We also use our QCA findings to uncover reasons behind the failure of firms to address developers’ expectation of evaluation which leads to TD. Our paper provides insights to managers and organizations that seek to improve resource investments for managing TD and avoiding low software quality.
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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".