Leveraging crowdsourcing for team elasticity: an empirical evaluation at TopCoder
Bibliographic record
Abstract
There is an emergent trend in software development projects that mini-tasks can be crowdsourced to achieve rapid development and delivery. For software managers requesting crowdsourcing services, it is beneficial to be able to evaluate and assure the availability and performance of trustable workers on their tasks. However, existing rating systems are facing challenges such as providing limited information regarding worker's abilities as well as potential threats from workers' gaming or cheating the systems. To develop better understanding of worker performance in software crowdsourcing, this paper reports an empirical study at TopCoder, one of the primary software crowdsourcing platforms. We aim at investigating the following questions: How diverse are crowd workers in terms of skill and experience? How fast do crowd workers respond to a task call? How reliable are crowd workers in submitting tasks? And how much does CSD benefit schedule reduction? The main results of this study showed that on average, (i) 59% of workers respond to a task call in the first 24 hours, (ii) 24% of the workers who registered early will make submissions to tasks, and 76% of them exceeding the acceptance criteria, and (iii) an overall average of 1.82 schedule acceleration rate is observed through organizing mass parallel development in 4 software crowdsourcing projects. Such empirical evidences are beneficial to help exploring resourcing options and improve team elasticity in adaptive software development.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".