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Software is playing the most important role in recent vehicle innovations, and consequently the amount of software has rapidly grown in recent decades. The safety-critical nature of ships, one sort of vehicle, makes software quality assurance (SQA) a fundamental prerequisite. Just-in-time software defect prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. The first case study of SDP in the maritime domain reported feasible prediction performance. However, we still consider that the prediction model has room for improvement since the parameters of the model are not optimized yet. Harmony search (HS) is a widely used music-inspired meta-heuristic optimization algorithm. In this article, we demonstrated that JIT-SDP can produce better performance of prediction by applying HS-based parameter optimization with balanced fitness value. Using two real-world datasets from the maritime software project, we obtained an optimized model that meets the performance criterion beyond the baseline of a previous case study throughout various defect to non-defect class imbalance ratio of datasets. Experiments with open source software also showed better recall for all datasets despite the fact that we considered balance as a performance index. HS-based parameter optimized JIT-SDP can be applied to the maritime domain software with a high class imbalance ratio. Finally, we expect that our research can be extended to improve the performance of JIT-SDP not only in maritime domain software but also in open source software.

This paper describes a questionanswering system that returns relevant documents and snippets (with particular emphasis on snippets) from a large medical document collection. The system is implemented as part of our participation to Phase A of Task 4b in the 2016 BioASQ Challenge. The proposed system retrieves candidate answer sentences using a cluster-based language model. Then, it re-ranks the retrieved top-n sentences using five independent similarity models based on shallow semantic analysis. The experimental results show that the proposed system is the first to find snippets in batches 2 (MAP 0.0604), 3 (MAP 0.0728), 4 (MAP 0.1182), and 5 (MAP 0.1187).

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