Context-Aware Personalized Crowdtesting Task Recommendation
Bibliographic record
Abstract
Crowdsourced software testing (short for crowdtesting) is a special type of crowdsourcing. It requires that crowdworkers master appropriate skill-sets and commit significant effort for completing a task. Abundant uncertainty may arise during a crowdtesting process due to imperfect information between the task requester and crowdworkers. For example, a worker frequently chooses tasks in an ad hoc manner in crowdtesting context, and an inappropriate task selection may lead to the worker's failing to detect any bugs, and significant testing effort unpaid and wasted. Recent studies have explored methods for supporting task requesters to make informed decisions on task pricing, worker recommendation, and so on. Unfortunately, very few study offers decision making support from the crowdworkers’ perspectives. We motivate this study through a pilot study, revealing the large portion (74 percent) of unpaid crowdworkers’ effort due to the inappropriate task choice. Drawn from our previous work on context-aware crowdworker recommendations, we advocate a more effective alternative to manual task selection would be to provide contextualized and personalized task recommendation considering the diverse distribution of worker preference and expertise, with objectives to increase their winning chances and to potentially reduce the frequency of unpaid crowd work. This paper proposes a context-aware personalized task recommendation approachPTRec, consisting of a testing context model and a learning-based task recommendation model to aid dynamic worker decision in selecting crowdtesting tasks. The testing context model is constructed in two perspectives, i.e., process context and resource context, to capture the in-process progress-oriented information and crowdworkers’ characteristics respectively. Built on top of this context model, the learning-based task recommendation model extracts 60 features automatically, and employs random forest learner to generate dynamic and personalized task recommendation which matches workers’ expertise and interest. The evaluation is conducted on 636 crowdtesting tasks involving 2,404 crowdworkers from one of the largest crowdtesting platforms, and results show our approach can achieve an average precision of 82 percent, average recall of 84 percent, and save an estimated average of 81 percent effort originally spent on exploring, significantly outperforming four commonly-used and state-of-the-art baselines. This indicates its potential in recommending proper tasks to workers so as to improve bug detection efficiency and increase their monetary earnings.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".