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Literature reviews can be time-consuming and tedious to complete. By cataloging and refactoring three state-of-the-art active learning techniques from evidence-based medicine and legal electronic discovery, this paper finds and implements FASTREAD, a faster technique for studying a large corpus of documents, combining and parametrizing the most efficient active learning algorithms. This paper assesses FASTREAD using datasets generated from existing SE literature reviews (Hall, Wahono, Radjenović, Kitchenham et al.). Compared to manual methods, FASTREAD lets researchers find 95% relevant studies after reviewing an order of magnitude fewer papers. Compared to other state-of-the-art automatic methods, FASTREAD reviews 20-50% fewer studies while finding same number of relevant primary studies in a systematic literature review.

StackOverflow (SO) contributors are recognized by reputation scores. Earning a high reputation score requires technical expertise and sustained effort. We analyzed the SO data from four perspectives to understand the dynamics of reputation building on SO. The results of our analysis provide guidance to new SO contributors who want to earn high reputation scores quickly. In particular, the results indicate that the following activities can help to build reputation quickly: answering questions related to tags with lower expertise density, answering questions promptly, being the first one to answer a question, being active during off peak hours, and contributing to diverse areas.

During software evolution a developer must investigate source code to locate then understand the entities that must be modified to complete a change task. To help developers in this task, Haiduc et al. proposed text summarization based approaches to the automatic generation of class and method summaries, and via a study of four developers, they evaluated source code summaries generated using their techniques. In this paper we propose a new topic modeling based approach to source code summarization, and via a study of 14 developers, we evaluate source code summaries generated using the proposed technique. Our study partially replicates the original study by Haiduc et al. in that it uses the objects, the instruments, and a subset of the summaries from the original study, but it also expands the original study in that it includes more subjects and new summaries. The results of our study both support the findings of the original and provide new insights into the processes and criteria that developers use to evaluate source code summaries. Based on our results, we suggest future directions for research on source code summarization.