A Replicated Survey of Software Testing Practices in the Canadian Province of Alberta: What has Changed from 2004 to 2009?
Bibliographic record
Abstract
Software organizations have typically de-emphasized the importance of software testing. In an earlier study in 2004, our colleagues reported the results of an Alberta-wide regional survey of software testing techniques in practice. Five years after that first study, the authors felt it is time to replicate the survey and analyze what has changed and what not from 2004 to 2009. This study was conducted during the summer of 2009 by surveying software organizations in the Canadian province of Alberta. The survey results reveal important and interesting findings about software testing Practices in Alberta, and point out what has changed from 2004 to 2009 and what not. Note that although our study is conducted in the province of Alberta, we have compared the results to few international similar studies, such as the ones conducted in the US, Turkey, Hong Kong and Australia, The study should thus be of interest to all testing professionals world-wide. Among the findings are the followings: (1) Almost all companies perform unit and system testing with a slight increase since 2004, (2) Automation of unit, integration and systems tests has increased sharply since 2004, (3) More organization are using observations and expert opinion to conduct usability testing, (4) The choices of test-case generation mechanisms have not changed much from 2004, (5) JUnit and IBM Rational tools are the most widely used test tools, (6) Alberta companies still face approximately the same defect-related economic issues as do companies in other jurisdictions, (7) Alberta software firms have improved their test automation capability since 2004, but there is still some room for improvement, and (8) Compared to 2004, more companies are spending more effort on pre-release testing.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".