CrowdAssist: A multidimensional decision support system for crowd workers

Abstract

Lately, crowdsourcing has emerged as a viable option for getting work done by leveraging the collective intelligence of the crowd. With many tasks posted every day, the size of crowdsourcing platforms is growing exponentially. Hence, workers face an important challenge in selecting the right task. Despite the task filtering criteria available on the platform to select the right task, crowd workers find it difficult to choose the most relevant task and must glean through the filtered tasks to find the relevant tasks. In this paper, we propose a framework for recommending tasks to workers. The proposed framework evaluates the worker's fitment over the tasks based on the worker's preference, past tasks he/she has performed, and tasks done by similar workers. We also proposed an approach to estimate the right price for a crowdsourced task for a specific worker. We evaluated our approach on the datasets collected from popular crowdsourcing platforms. Our experimental results show that the recommendation made by our framework for task and price is significantly better as compared with the baseline approach.

Editorial: Machine learning, software process, and global software engineering

“…The third contribution to this special issue, “CrowdAssist: A multi‐dimensional decision support system for crowd workers” 3 , addresses the area of crowdsourcing software development work, acknowledging that challenges arise when aligning developers with posted tasks and with the identification of appropriate task pricing. Individual developer preferences, past tasks, and tasks performed by similar developers are all considered.…”

Requirement Dependency Extraction Based on Improved Stacking Ensemble Machine Learning

“…Another important role of machine learning is to reduce manual workloads in software engineering tasks [16][17][18][19][20], such as defect prediction, code suggestions, automatic program repair, feature localization and malware detection. Machine learning has also been widely applied in cost prediction, software testing and software quality assessment in the software development process, such as in consistency research between developers and tasks [21], integration testing [22], software development cost prediction [23] and software quality assessment [24]. Meanwhile, requirements engineering has also applied a large number of machine learning methods [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], such as requirement acquisition, requirement formalization, requirement classification, the identification of software vulnerabilities from requirement specifications, requirement prioritization, requirement dependency extraction and requirement management.…”