A Complex Adaptive Systems Perspective of Software Reuse in the Digital Age: An Agenda for IS Research
Bibliographic record
Abstract
Most software on which we rely to help us organize our professional and personal lives is based on the reuse of other pieces of software that are created and maintained by groups of software developers that work independently from one another. Oftentimes, these groups simply publish their software in the form of self-contained packages available on dedicated repositories, facilitating the widespread diffusion of their work. Whereas the production and publication of software packages fosters unprecedented levels of digital innovation, there are also drawbacks associated with software reuse (e.g., as was publicly discussed in 2021 with the discovery of the Log4Shell vulnerability). Building on previous research, our work explores the implications associated with the unprecedented scale and uncoordinated nature of packaged software reuse. We use complex adaptive systems as a generative lens to help us conceptualize the phenomenon and identify promising avenues for research and practice on this topic. Our work, therefore, draws attention to the importance of the packaged software reuse phenomenon as well as the need for research to help increase our understanding of its nature and implications considering its prevalence in software development practice and the overall importance of software in our everyday lives.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".