دانلود رایگان مقاله انگلیسی ARSENAL-GSD: چارچوب برآورد اعتماد در تیم های مجازی بر اساس تحلیل احساسات - الزویر 2017

ABSTRACT

Context: Technology advances has enabled the emergence of virtual teams. In these teams, people are in different places and possibly over different time zones, making use of computer mediated communication to interact. At the same time distribution brings benefits, it poses challenges as the difficulty to develop trust, which is essential for team efficiency. Objective: In this paper, we present ARSENAL-GSD, an automatic framework for detecting trust among members of global software development teams based on sentiment analysis. Methods: To design ARSENAL-GSD we made a literature review to identify trust evidences, especially those that could be captured or inferred from the automatic analysis of data generated by members’ interactions in a versioning system. We applied a survey to validate the framework and evidences found. Results: On a scale of 0–9, evidences were evaluated as having importance greater or equal to 5.23, and the extraction techniques used to estimate them were considered as good enough. Regarding differences between subjects profile, no difference was found in responses of participants with theoretical knowledge/none and those with medium/high knowledge in GSD, except for the evidence mimicry, which was considered more important for the group of participants with medium/high knowledge in GSD. Conclusion: We concluded that our framework is valid and trust information provided by it could be used to allocate members to a new team and/or, to monitor them during project development.

7. Conclusions

The efficiency of a GSD team is directly tied to trust among team members. The higher is the trust, the lower is the project costs. Trust also increases communication and facilitate cooperation, coordination, knowledge and information sharing, which improve the quality of generated products.

Motivated by the importance of trust for these teams, we presented an automatic framework to estimate trust existence among members of a GSD team. It uses versioning systems, a collaborative tool used in software development, as a data source. To design the framework, we used some of the trust evidences presented in the literature that can be extracted from versioning systems data. One of the main features of the proposed framework is the use of sentiment analysis to extract some of these evidences, for example, the positive tone of the conversations.

The main contribution of this paper is in the mapping of trust evidences and elements of trust models that can be captured using sentiment analysis. We expect ARSENAL-GSD to provide a better estimative of trust existence than general automatic models in the literature since it uses sentiment analysis and a rich set of evidences. Employing sentiment analysis enables us to extract something unique for each person, thus adding subjectivity to our estimative, which is an important characteristic of trust. This subjectivity cannot be captured with the use of metrics, which are generally used in automatic models. GSD managers can benefit from ARSENAL-GSD to create teams with higher trust levels and to monitor trust level variations, so actions can be taken when the trust level decreases.