The standard approach to applying text retrieval models to code repositories is to train models on documents representing program elements. However, code changes lead to model obsolescence and to the need to retrain the model from the latest snapshot. To address this, we previously introduced an approach that trains a model on documents representing changesets from a repository and demonstrated its feasibility for feature location. In this paper, we expand our work by investigating: a second task (developer identification), the effects of including different changeset parts in the model, the repository characteristics that affect the accuracy of our approach, and the effects of the time invariance assumption on evaluation results. Our results demonstrate that our approach is as accurate as the standard approach for projects with most changes localized to a subset of the code, but less accurate when changes are highly distributed throughout the code. Moreover, our results demonstrate that context and messages are key to the accuracy of changeset-based models and that the time invariance assumption has a statistically significant effect on evaluation results, providing overly-optimistic results. Our findings indicate that our approach is a suitable alternative to the standard approach, providing comparable accuracy while eliminating retraining costs.

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.

This document contains the definition of a rubric used to classify security research papers. First we define three dimensions used to classify each paper: a) what is being analyzed in the paper - the evaluation subject, b) whether the evaluation subject is new, meaning whether it was first proposed in this paper or not

Developers often rely on the web-based tools for troubleshooting, collaboration, issue tracking, code reviewing, documentation viewing, and a myriad of other uses. Developers also use the web for non-development purposes, such as reading news or social media. In this paper we explore whether web usage is detriment to a developer's focus on work from a sample over 150 developers. Additionally, we investigate if highly-focused developers use the web differently than other developers. Our qualitative findings suggest highly-focused developers use the web differently, but we are unable to predict a developer's focused based on web usage alone. Further quantitative findings suggest that web usage does not have a negative impact on a developer's focus.

Feature location is a program comprehension activity in which a developer inspects source code to locate the classes or methods that implement a feature of interest. Many feature location techniques (FLTs) are based on text retrieval models, and in such FLTs it is typical for the models to be trained on source code snapshots. However, source code evolution leads to model obsolescence and thus to the need to retrain the model from the latest snapshot. In this paper, we introduce a topic-modeling-based FLT in which the model is built incrementally from source code history. By training an online learning algorithm using changesets, the FLT maintains an up-to-date model without incurring the non-trivial computational cost associated with retraining traditional FLTs. Overall, we studied over 600 defects and features from 4 open-source Java projects. We also present a historical simulation that demonstrates how the FLT performs as a project evolves. Our results indicate that the accuracy of a changeset-based FLT is similar to that of a snapshot-based FLT, but without the retraining costs.

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.

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 topic-document 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.

Daniel German, University of Victoria, Canada Abram Hindle, University of Alberta, Canada Bram Adams, Ecole Polytechnique de Montreal, Canada Dave Binkley, Loyola University Maryland, USA Michael Collard, The University of Akron, USA Christopher Corley, The University of Alabama, USA Andrea De Lucia, University of Salerno, Italy Coen De Roover, Osaka University, Japan Marios Fokaefs, University of Alberta, Canada Nicolas Gold, University College London, UK Scott Grant, Queen's University, Canada Mark Grechanik, University of Illinois at Chicago, Chicago Emily Hill, Montclair State University, USA Foutse Khomh, Ecole Polytechnique de Montreal, Canada Kostas Kontogiannis, University of Waterloo, Canada / NTUA, Greece Nicholas Kraft, The University of Alabama, USA Jens Krinke, University College London, UK Shane Mcintosh, Queen's University, Canada Ali Mesbah, University of British Columbia, Canada Denys Poshyvanyk, College of William and Mary, USA Peter Rigby, Concordia University, Canada Emad Shihab, Rochester Institute of Technology, USA

Topic modeling has been applied to several areas of software engineering, such as bug localization, feature location, triaging change requests, and traceability link recovery. Many of these approaches combine mining unstructured data, such as bug reports, with topic modeling a snapshot (or release) of source code. However, source code evolves, which causes models to become obsolete. In this paper, we explore the approach of topic modeling changesets over the traditional release approach. We conduct an exploratory study of four open source systems. We investigate the differences in corpora in each project, and evaluate the topic distinctness of the models.

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.

Exploring linguistic topics in source code is a program comprehension activity that shows promise in helping a developer to become familiar with an unfamiliar software system. Examining ownership in source code can reveal complementary information, such as who to contact with questions regarding a source code entity, but the relationship between linguistic topics and ownership is an unexplored area. In this paper we combine software repository mining and topic modeling to measure the ownership of linguistic topics in source code. We conduct an exploratory study of the relationship between linguistic topics and ownership in source code using 10 open source Java systems. We find that classes that belong to the same linguistic topic tend to have similar ownership characteristics, which suggests that conceptually related classes often share the same owner(s). We also find that similar topics tend to share the same ownership characteristics, which suggests that the same developers own related topics.

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.