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Abstract-Since developer ability is recognized as a determinant of better software project performance, it is a critical step to model and evaluate the programming ability of developers. However, most existing approaches require manual assessment, like 360 degree performance evaluation. With the emergence of social networking sites such as StackOverflow and Github, a vast amount of developer information is created on a daily basis. Such personal and social context data has huge potential to support automatic and effective developer ability evaluation. In this paper, we propose CPDScorer, a novel approach to modeling and scoring the programming ability of developer through mining heterogeneous information from both Community Question Answering (CQA) sites and Open-Source Software (OSS) communities. CPDScorer analyzes the answers posted in CQA sites and evaluates the projects submitted in OSS communities to assign expertise scores to developers, considering both the quantitative and qualitative factors. When modeling the programming ability of developer, a programming ability term extraction algorithm is also designed based on set covering. We have conducted experiments on StackOverflow and Github to measure the effectiveness of CPDScorer. The results show that our approach is feasible and practical in user programming ability modeling. In particular, the precision of our approach reaches 80%.

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Abstract-Recently, software crowdsourcing platforms, which provide paid tasks for developers, become attractive to both employers and developers. Developers expect to find tasks that match their interests and capabilities via crowdsourcing platforms, and thus recommender systems play important roles in these platforms. However, we still face several challenges when building a recommender system for a crowdsourcing platform. A major challenge is how to recommend tasks to cold-start developers whose task interaction data is not available. This paper presents a novel, topic sampling approach to tackling with the cold-start developer recommendation problem. First, it employs a general method for modeling developers and tasks, which solves the data heterogeneous issue across different platforms. After that, it casts the cold-start developer recommendation problem into a multi-optimization problem, and takes a topic-sampling based genetic algorithm to recommend tasks. More specifically, our approach is different from traditional solutions in that it leverages task descriptions and popularity-to-be, allowing new tasks to be recommended to cold-start developers. To evaluate the effectiveness of the proposed approach, we have conducted experiments on a large dataset crawled from three real-world software crowdsourcing platforms. Compared with other state-ofthe-art recommendation solutions, the experimental results show that the proposed approach improves 75% of precision and recall on average.

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