Context- and Fairness-Aware In-Process Crowdworker Recommendation

Junjie Wang

doi:10.1145/3487571

Context- and Fairness-Aware In-Process Crowdworker Recommendation

2022-03-07

13

OA

AI

DOI：10.1145/3487571

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En 中文

Identifying and optimizing open participation is essential to the success of open software development. Existing studies highlighted the importance of worker recommendation for crowdtesting tasks in order to improve bug detection efficiency, i.e., detect more bugs with fewer workers. However, there are a couple of limitations in existing work. First, these studies mainly focus on one-time recommendations based on expertise matching at the beginning of a new task. Second, the recommendation results suffer from severe popularity bias, i.e., highly experienced workers are recommended in almost all the tasks, while less experienced workers rarely get recommended. This article argues the need for context- and fairness-aware in-process crowdworker recommendation in order to address these limitations. We motivate this study through a pilot study, revealing the prevalence of long-sized non-yielding windows, i.e., no new bugs are revealed in consecutive test reports during the process of a crowdtesting task. This indicates the potential opportunity for accelerating crowdtesting by recommending appropriate workers in a dynamic manner, so that the non-yielding windows could be shortened. Besides, motivated by the popularity bias in existing crowdworker recommendation approach, this study also aims at alleviating the unfairness in recommendations. Driven by these observations, this article proposes a context- and fairness-aware in-process crowdworker recommendation approach, iRec2.0, to detect more bugs earlier, shorten the non-yielding windows, and alleviate the unfairness in recommendations. It consists of three main components: (1) the modeling of dynamic testing context, (2) the learning-based ranking component, and (3) the multi-objective optimization-based reranking component. The evaluation is conducted on 636 crowdtesting tasks from one of the largest crowdtesting platforms, and results show the potential of iRec2.0 in improving the cost-effectiveness of crowdtesting by saving the cost, shortening the testing process, and alleviating the unfairness among workers. In detail, iRec2.0 could shorten the non-yielding window by a median of 50%-66% in different application scenarios, and consequently have potential of saving testing cost by a median of 8%-12%. Meanwhile, the recommendation frequency of the crowdworker drop from 34%-60% to 5%-26% under different scenarios, indicating its potential in alleviating the unfairness among crowdworkers.

Keyword:

Crowdsourced testing

worker recommendation

multi-objective optimization

fair recommendation

Context- and Fairness-Aware In-Process Crowdworker Recommendation

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