Towards Actionable Visualization for Software Developers

Abundant studies have shown that visualization is advantageous for software developers, yet adopting visualization during software development is not a common practice due to the large effort involved in finding an appropriate visualization. Developers require support to facilitate that task. Among 368 papers in SOFTVIS/VISSOFT venues, we identify 86 design study papers about the application of visualization to relieve concerns in software development. We extract from these studies the task, need, audience, data source, representation, medium and tool; and we characterize them according to the subject, process and problem domain. On the one hand, we support software developers to put visualization in action by mapping existing visualization techniques to particular needs from different perspectives. On the other hand, we highlight the problem domains that are overlooked in the field and need more support

Supporting Streams of Changes during Branch Integration

International audienceWhen developing large applications, integrators face the problem of integrating changes between branches or forks. While version control systems provide support for merging changes, this support is mostly text-based, and does not take the program entities into account. Furthermore, there exists no support for assessing which other changes a particular change depends on have to be integrated. Consequently, integrators are left to perform a manual and tedious comparison of the changes within the sequence of their branch and to successfully integrate them. In this paper, we present an approach that analyzes changes within a sequence of changes (stream of changes): such analysis identifies and characterizes dependencies between the changes. The approach identifies changes as autonomous, only used by others, only using other changes, or both. Such a characterization aims at easing the integrator's work. In addition, the approach supports important queries that an integrator otherwise has to perform manually. We applied the approach to a stream of changes representing 5 years of development work on an open- source project and report our experiences

What kind of questions do developers ask on Stack Overflow? A comparison of automated approaches to classify posts into question categories

On question and answer sites, such as Stack Overflow (SO), developers use tags to label the content of a post and to support developers in question searching and browsing. However, these tags mainly refer to technological aspects instead of the purpose of the question. Tagging questions with their purpose can add a new dimension to the identification of discussed topics in posts on SO. In this paper, we aim at automating the classification of SO question posts into seven question categories. As a first step, we harmonized existing taxonomies of question categories and then, we manually classified 1,000 SO questions according to our new taxonomy. Additionally to the question category, we marked the phrases that indicate a question category for each of the posts. We then use this data set to automate the classification of posts using two approaches. For the first approach, we manually analyzed the phrases to find patterns. Based on regular expressions, we implemented a classifier, for each of the categories, that determines whether a post belongs to a category. These regular expressions are derived by analyzing patterns in the phrases. In the second approach, we use the curated data set to train classification models of supervised machine learning algorithms (Random Forest and Support Vector Machines). For the machine learning algorithms, we experimented with 1,312 different configurations regarding the preprocessing of the text and the representation of the input data. Then, we compared the performance of the regex approach with the performance of the best configuration that uses machine learning algorithms on a validation set of 110 posts. The results show that using the regular expression approach, we can classify posts into the correct question category with an average precision and recall of 0.90, and an MCC of 0.68. Additionally, we applied the regex approach on all questions of SO that deal with Android app development and investigated the co-occurrence of question categories in posts. We found that the categories API usage, Conceptual, and Discrepancy are the most frequently assigned question categories and that they also occur together frequently. Our approach can be used to support developers in browsing SO discussions or researchers in building recommender systems based on SO

The whats and hows of programmers' foraging diets

One of the least studied areas of Information Foraging Theory is diet: the information foragers choose to seek. For example, do foragers choose solely based on cost, or do they stubbornly pursue certain diets regardless of cost? Do their debugging strategies vary with their diets? To investigate "what" and "how" questions like these for the domain of software debugging, we qualitatively analyzed 9 professional developers' foraging goals, goal patterns, and strategies. Participants spent 50% of their time foraging. Of their foraging, 58% fell into distinct dietary patterns—mostly in patterns not previously discussed in the literature. In general, programmers' foraging strategies leaned more heavily toward enrichment than we expected, but different strategies aligned with different goal types. These and our other findings help fill the gap as to what programmers' dietary goals are and how their strategies relate to those goals.This tech report is an extended version of publication [17]: It adds Appendix A to the end. © ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proc. CHI, ACM (2013)Keywords: Information foraging theory, information diet, User/Machine Systems -- Human factors, Testing and Debugging, debugging strategie