David Piorkowski

An Information Foraging Theory Perspective on Tools for Debugging, Refactoring, and Reuse Tasks

Theories of human behavior are an important but largely untapped resource for software engineering research. They facilitate understanding of human developers’ needs and activities, and thus can serve as a valuable resource to researchers designing software engineering tools. Furthermore, theories abstract beyond specific methods and tools to fundamental principles that can be applied to new situations. Toward filling this gap, we investigate the applicability and utility of Information Foraging Theory (IFT) for understanding information-intensive software engineering tasks, drawing upon literature in three areas: debugging, refactoring, and reuse. In particular, we focus on software engineering tools that aim to support information-intensive activities, that is, activities in which developers spend time seeking information. Regarding applicability, we consider whether and how the mathematical equations within IFT can be used to explain why certain existing tools have proven empirically successful at helping software engineers. Regarding utility, we applied an IFT perspective to identify recurring design patterns in these successful tools, and consider what opportunities for future research are revealed by our IFT perspective.

Modeling programmer navigation: A head-to-head empirical evaluation of predictive models

Software developers frequently need to perform code maintenance tasks, but doing so requires time-consuming navigation through code. A variety of tools are aimed at easing this navigation by using models to identify places in the code that a developer might want to visit, and then providing shortcuts so that the developer can quickly navigate to those locations. To date, however, only a few of these models have been compared head-to-head to assess their predictive accuracy. In particular, we do not know which models are most accurate overall, which are accurate only in certain circumstances, and whether combining models could enhance accuracy. Therefore, we have conducted an empirical study to evaluate the accuracy of a broad range of models for predicting many different kinds of code navigations in sample maintenance tasks. Overall, we found that models tended to perform best if they took into account how recently a developer has viewed pieces of the code, and if models took into account the spatial proximity of methods within the code. We also found that the accuracy of single-factor models can be improved by combining factors, using a spreading-activation based approach, to produce multi-factor models. Based on these results, we offer concrete guidance about how these models could be used to provide enhanced software development tools that ease the difficulty of navigating through code.

Foraging Among an Overabundance of Similar Variants

Foraging among too many variants of the same artifact can be problematic when many of these variants are similar. This situation, which is largely overlooked in the literature, is commonplace in several types of creative tasks, one of which is exploratory programming. In this paper, we investigate how novice programmers forage through similar variants. Based on our results, we propose a refinement to Information Foraging Theory (IFT) to include constructs about variation foraging behavior, and propose refinements to computational models of IFT to better account for foraging among variants.

Putting information foraging theory to work: Community-based design patterns for programming tools

The design of programming tools is slow and costly. To ease this process, we developed a design pattern catalog aimed at providing guidance for tool designers. This catalog is grounded in Information Foraging Theory (IFT), which empirical studies have shown to be useful for understanding how developers look for information during development tasks. New design patterns, authored by members of the research community for the catalog, concretely explain how to apply IFT in tool design. In our evaluation, qualitative analyses revealed the community-written design patterns compared well in quality to patterns that we had ourselves published in a smaller, peer-reviewed catalog.

PFIS-V: Modeling Foraging Behavior in the Presence of Variants

Foraging among similar variants of the same artifact is a common activity, but computational models of Information Foraging Theory (IFT) have not been developed to take such variants into account. Without being able to computationally predict peoples foraging behavior with variants, our ability to harness the theory in practical ways--such as building and systematically assessing tools for people who forage different variants of an artifact--is limited. Therefore, in this paper, we introduce a new predictive model, PFIS-V, that builds upon PFIS3, the most recent of the PFIS family of modeling IFT in programming situations. Our empirical results show that PFIS-V is up to 25% more accurate than PFIS3 in predicting where a forager will navigate in a variationed information space.

Foraging goes mobile: Foraging while debugging on mobile devices

Although Information Foraging Theory (IFT) research for desktop environments has provided important insights into numerous information foraging tasks, we have been unable to locate IFT research for mobile environments. Despite the limits of mobile platforms, mobile apps are increasingly serving functions that were once exclusively the territory of desktops — and as the complexity of mobile apps increases, so does the need for foraging. In this paper we investigate, through a theory-based, dual replication study, whether and how foraging results from a desktop IDE generalize to a functionally similar mobile IDE. Our results show ways prior foraging research results from desktop IDEs generalize to mobile IDEs and ways they do not, and point to challenging open research questions for foraging on mobile environments.

Visual Configuration of Mobile Privacy Policies

Mobile applications often require access to private user information, such as the user or device ID, the location or the contact list. Usage of such data varies across different applications. A notable example is advertising. For contextual advertising, some applications release precise data, such as the users exact address, while other applications release only the users country. Another dimension is the user. Some users are more privacy demanding than others. Existing solutions for privacy enforcement are neither app- nor user- sensitive, instead performing general tracking of private data into release points like the Internet. The main contribution of this paper is in refining privacy enforcement by letting the user configure privacy preferences through a visual interface that captures the applications screens enriched with privacy-relevant information. We demonstrate the efficacy of our approach w.r.t. advertising and analytics, which are the main third-party consumers of private user information. We have implemented our approach for Android as the VisiDroid system. We demonstrate VisiDroids efficacy via both quantitative and qualitative experiments involving top-popular Google Play apps. Our experiments include objective metrics, such as the average number of configuration actions per app, as well as a user study to validate the usability of VisiDroid.