Launch pad

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Launch Pad Introduction

Educación Tecnología Empresariales

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Written development communication (e.g. mailing lists, issue trackers) constitutes a precious source of information to build recommenders for software engineers, for example aimed at suggesting experts, or at redocumenting existing source code. In this paper we propose a novel, semi-supervised approach named DECA (Development Emails Content Analyzer) that uses Natural Language Parsing to classify the content of development emails according to their purpose (e.g. feature request, opinion asking, problem discovery, solution proposal, information giving etc), identifying email elements that can be used for specific tasks. A study based on data from Qt and Ubuntu, highlights a high precision (90%) and recall (70%) of DECA in classifying email content, outperforming traditional machine learning strategies. Moreover, we successfully used DECA for re-documenting sourcecode of Eclipse and Lucene, improving the recall, while keeping high precision, of a previous approach based on ad-hoc heuristics.

Development Emails Content Analyzer: Intention Mining in Developer Discussions

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The mobile applications industry experiences an unprecedented high growth, developers working in this context face a fierce competition in acquiring and retaining users. They have to quickly implement new features and fix bugs, or risks losing their users to the competition. To achieve this goal they must closely monitor and analyze the user feedback they receive in form of reviews. However, successful apps can receive up to several thousands of reviews per day, manually analysing each of them is a time consuming task. To help developers deal with the large amount of available data, we manually analyzed the text of 1566 user reviews and defined a high and low level taxonomy containing mobile specific categories (e.g. performance, resources, battery, memory, etc.) highly relevant for developers during the planning of maintenance and evolution activities. Then we built the User Request Referencer (URR) prototype, using Machine Learning and Information Retrieval techniques, to automatically classify reviews according to our taxonomy and recommend for a particular review what are the source code files that need to be modified to handle the issue described in the user review. We evaluated our approach through an empirical study involving the reviews and code of 39 mobile applications. Our results show a high precision and recall of URR in organising reviews according to the defined taxonomy

Analyzing Reviews and Code of Mobile Apps for Better Release Planning

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The recent and fast expansion of OSS (Open-source software) communities has fostered research on how open source projects evolve and how their communities interact. Several research studies show that the inflow of new developers plays an important role in the longevity and the success of OSS projects. Beside that they also discovered that an high percentage of newcomers tend to leave the project because of the socio-technical barriers they meet when they join the project. However, such research effort did not generate yet concrete results in support retention and training of project newcomers. In this thesis dissertation we investigated problems arising when newcomers join software projects, and possible solutions to support them. Specifically, we studied (i) how newcomers behave during development activities and how they interact with others developers with the aim at (ii) developing tools and/or techniques for supporting them during the integration in the development team. Thus, among the various recommenders, we defined (i) a tool able to suggest appropriate mentors to newcomers during the training stage; then, with the aim at supporting newcomers during program comprehension we defined other two recommenders: a tool that (ii) generates high quality source code summaries and another tool able to (iii) provide descriptions of specific source code elements. For future work, we plan to improve the proposed recommenders and to integrate other kind of recommenders to better support newcomers in OSS projects.

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Launch pad

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