Software maintenance is an essential phase of software development. Developers employ issue tracking systems to collect bugs for software improvement. Users submit bugs through such issue tracking systems and decide the severity of reported bugs. The severity is an important attribute of a bug that decides how quickly it should be solved. It helps developers to solve important bugs on time. However, manual severity assessment is a tedious job and could be incorrect. To this end, in this paper, we propose a deep neural network-based automatic approach for the severity prediction of bug reports. First, we apply natural language processing techniques for text preprocessing of bug reports. Second, we compute and assign an emotion score for each bug report. Third, we create a vector for each preprocessed bug report. Forth, we pass the constructed vector and the emotion score of each bug report to a deep neural network based classifier for severity prediction. We also evaluate the proposed approach on the history-data of bug reports. The results of cross-product suggest that the proposed approach outperforms the state-of-the-art approaches. On average, it improves the f-measure by 7.90%.

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In the context of natural-language processing, keyword extraction has been studied widely. In promoting businessenterprise goods and services, however, a major challenge remains to extracting keywords effectively and efficiently from social-media user-generated data, wherein employed are traditional, language-dependent and supervised keyword-extraction techniques. This study contributes a keyword extraction analytic hierarchy process (KEAHP), as a language-independent and unsupervised keyword-extraction technique. By using four user-generated data attributes, KEAHP identifies keywords from the word co-occurrence in linguistic networks, based on a multiple-attribute decision-making approach. The proposed technique has been validated via a publically-available standard dataset, and the experimental results show the effectiveness and efficiency of the algorithm in KEAHP. Despite its limitations, the study contends that KEAHP can drastically improve performance in promoting business-enterprise goods and services, while also discussed are implications for future research and practice in keyword-extraction techniques.

Crowdsourcing is gaining more and more popularity among the academic and industrial community. Organizations are adopting this technological advent and increasingly crowdsourcing their tasks to the unknown individuals. However, in the context of competitive crowdsourcing software development (CCSD), crowdsourcing is still unexplored. Too little is presently known about what intricate developers to participate in crowdsourcing software development competitions. Most importantly, what kind of developers are more likely to participate? Such open questions remain to be explored. To this end, in this paper, we present the results of an empirical study conducted to investigate what motivates software developers to participate in CCSD and what inhibits software developers to participate in such competitions. An online questionnaire is sent out to more than 300 crowdsource software participants, of which 113 return valid responses. It is also sent to more than 150 industry practitioners, of which 75 return valid responses. The results suggest that the monetary rewards are not significantly important to motivate software developers to participate in CCSD. Instead, learning, social contacts, and peer recognition are more important. Besides the survey, we also analyze the historical data collected from one of the most popular software crowdsourcing platforms. The analysis results reveal that the Pareto principle holds for CCSD as well, and 0.9% of the participants win 86% competitions. The results support the premise that CCSD market is still at an early stage. Most of the professional software engineers do not participate seriously in crowdsourcing software development. Therefore, many crowdsourced tasks, especially complex tasks, may fail to receive any satisfying submission. These findings are worthwhile for the crowdsourcing platforms and companies who want to outsource their software development tasks to the CCSD platforms. INDEX TERMS Crowdsourcing, motivation, inhibiting factors, competitive software development.

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