The Evolution of Software Design Practices Over a Decade: A Long Term Study of Practitioners.
Bibliographic record
Abstract
We present the results of a survey of 248 software practitioners conducted in three phases ten years apart.The goal of the study is to uncover trends in the practice of software design and the adoption patterns of modeling languages such as UML.The first phase was conducted in April-December 2007 and included 113 responses.The second phase was conducted in March-November 2017 and included 115 responses.The third phase is a post-survey study was conducted in November 2018 and included additional questionnaires with 20 participants.All survey phases were conducted online, employed identical solicitation mechanisms, and included the same set of questions.The survey results are analyzed within each phase and across phases.We present the results and analysis of the data identifying upward and downward trends in design and modeling practices.The results indicate a significant increase in the use of well-defined and formal modeling languages, as well as a marked increase in the adoption of Domain-Specific Languages.This is also reflected in a significant increase in the adoption of forward engineering methodologies.A key motivation for this uptake is a concern that programming languages and platforms may become quickly outdated.Unfortunately, there has been a consistent dissatisfaction with modeling tools features, particularly their ability to support effective communication and collaboration.This is mirrored by increasing dissatisfaction with modeling tools usability and learnability.Future projections of this study suggest that diversity in modeling languages and tools is likely to continue to grow, as well as the increase in reliance on models for automated artifacts generation.As such, model and tool interoperability is likely to become an even greater concern for the years to come.The results of this study can help researchers, practitioners, and educators to focus efforts on issues of relevance and significance to the profession.Specifically, this research will advocate to build better software modeling tools and promote modeling to the educators.
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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".