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.
Deep learning models can infer complex patterns present in natural language text. Relative to n-gram models, deep learning models can capture more complex statistical patterns based on smaller training corpora. In this paper we explore the use of a particular deep learning model, document vectors (DVs), for feature location. DVs seem well suited to use with source code, because they both capture the influence of context on each term in a corpus and map terms into a continuous semantic space that encodes semantic relationships such as synonymy. We present preliminary results that show that a feature location technique (FLT) based on DVs can outperform an analogous FLT based on latent Dirichlet allocation (LDA) and then suggest several directions for future work on the use of deep learning models to improve developer effectiveness in feature location.
Traceability links can be recovered using data mined from a revision control system, such as CVS, and an issue tracking system, such as Bugzilla. Existing approaches to recover links between a bug and the methods changed to fix the bug rely on the presence of the bug's identifier in a CVS log message. In this paper we present an approach that relies instead on the presence of a patch in the issue report for the bug. That is, rather than analyzing deltas retrieved from CVS to recover links, our approach analyzes patches retrieved from Bugzilla. We use BugTrace, the tool implementing our approach, to conduct a case study in which we compare the links recovered by our approach to links recovered by manual inspection. The results of the case study support the efficacy of our approach. After describing the limitations of our case study, we conclude by reviewing closely related work and suggesting possible future work.
Due to the high number and cost of interruptions at work, several approaches have been suggested to reduce this cost for knowledge workers. These approaches predominantly focus either on a manual and physical indicator, such as headphones or a closed office door, or on the automatic measure of a worker's interruptibilty in combination with a computer-based indicator. Little is known about the combination of a physical indicator with an automatic interruptibility measure and its long-term impact in the workplace. In our research, we developed the FlowLight, that combines a physical traffic-light like LED with an automatic interruptibility measure based on computer interaction data. In a large-scale and long-term field study with 449 participants from 12 countries, we found, amongst other results, that the FlowLight reduced the interruptions of participants by 46%, increased their awareness on the potential disruptiveness of interruptions and most participants never stopped using it. ABSTRACTDue to the high number and cost of interruptions at work, several approaches have been suggested to reduce this cost for knowledge workers. These approaches predominantly focus either on a manual and physical indicator, such as headphones or a closed office door, or on the automatic measure of a worker's interruptibilty in combination with a computer-based indicator. Little is known about the combination of a physical indicator with an automatic interruptibility measure and its long-term impact in the workplace. In our research, we developed the FlowLight, that combines a physical traffic-light like LED with an automatic interruptibility measure based on computer interaction data. In a large-scale and long-term field study with 449 participants from 12 countries, we found, amongst other results, that the FlowLight reduced the interruptions of participants by 46%, increased their awareness on the potential disruptiveness of interruptions and most participants never stopped using it.
Issue tracking software of large software projects receive a large volume of issue reports each day. Each of these issues is typically triaged by hand, a time consuming and error prone task. Additionally, issue reporters lack the necessary understanding to know whether their issue has previously been reported. This leads to issue trackers containing a lot of duplicate reports, adding complexity to the triaging task.Duplicate bug report detection is designed to aid developers by automatically grouping bug reports concerning identical issues. Previous work by Alipour et al. has shown that the textual, categorical, and contextual information of an issue report are effective measures in duplicate bug report detection. In our work, we extend previous work by introducing a range of metrics based on the topic distribution of the issue reports, relying only on data taken directly from bug reports. In particular, we introduce a novel metric that measures the first shared topic between two topicdocument distributions. This paper details the evaluation of this group of pair-based metrics with a range of machine learning classifiers, using the same issues used by Alipour et al. We demonstrate that the proposed metrics show a significant improvement over previous work, and conclude that the simple metrics we propose should be considered in future studies on bug report deduplication, as well as for more general natural language processing applications.
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