Who should pay for technical debt? Exploring software professionals perceptions about technical debt accountability
Bibliographic record
Abstract
Technical debt (TD) highlights the consequences of suboptimal design decisions made during Information Systems (IS) development. Despite reducing IS quality, if taken strategically and managed proactively, TD enables firms to gain a competitive advantage in the short-term. However, if taken without strategic intent and left unresolved, TD can lead to significant costs in the long-term. Previous studies mainly examine TD accumulation at the organizational level and its latent costs to the organization. However, considering the crucial role of individuals in IS development, further research is needed to provide us with a theoretical understanding of TD that is accumulated because of unnecessary shortcuts taken by software professionals without any strategic intent. To explore this costly concern, we interviewed 25 software professionals across industry domains and from all three global regions. Using accountability theory as a lens, we conducted thematic analysis and qualitative comparative analysis to uncover the participants' perceptions of responsibilities and accountability issues associated with the accumulation and management of TD. Our analysis shows that software professionals' perception of TD accountability is influenced by 1) the extent to which prospective and retrospective accountability mechanisms are established in organizations and the way they are followed (i.e., bureaucratically vs. democratically) and 2) the extent to which collective culture emphasizes the importance of ensuring software quality and promotes compliance with quality rules. Thus, we propose TD accountability as a crucial coordination and consensus building mechanism for promoting a quality culture in development teams and facilitating appropriate accumulation and management of TD in organizations. In addition to contributing to IS literature, we provide insights for organizations to coordinate the accumulation and management of TD. • Technical debt (TD) indicates the accrued liability of suboptimal design decisions. • We show the importance of differentiating between coordinated and uncoordinated TD. • We offer a novel account of perceived TD accountability and responsibilities. • TD accountability can serve as a coordination and consensus building mechanism. • We recommend firms enact TD accountability democratically, not bureaucratically.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".