To fix or to learn? How production bias affects developers' information foraging during debugging

Piorkowski, David; Fleming, Scott; Scaffidi, Christopher; Burnett, Margaret; Kwan, Irwin; Henley, Austin Z.; Macbeth, Jamie; Hill, Charles G.; Horvath, Amber

doi:10.1109/icsm.2015.7332447

Cited by 32 publications

(19 citation statements)

References 6 publications

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“…However, no previous work proposes the sharing of knowledge related to debugging activities. Differently from works that use IFT on a model one prey/one predator [28], we are interested in many developers working independently in many debugging sessions and sharing information to allow SI to emerge. Thus, debugging becomes a foraging process in a SI environment.…”

Section: Self-organization and Swarm Intelligencementioning

confidence: 99%

Swarm debugging: The collective intelligence on interactive debugging

Petrillo

Guéhéneuc

Pimenta

et al. 2019

Journal of Systems and Software

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One of the most important tasks in software maintenance is debugging. To start an interactive debugging session, developers usually set breakpoints in an integrated development environment and navigate through different paths in their debuggers. We started our work by asking what debugging information is useful to share among developers and study two pieces of information: breakpoints (and their locations) and sessions (debugging paths).To answer our question, we introduce the Swarm Debugging concept to frame the sharing of debugging information, the Swarm Debugging Infrastructure (SDI) with which practitioners and researchers can collect and share data about developers' interactive debugging sessions, and the Swarm Debugging Global View (GV) to display debugging paths. Using the SDI, we conducted a large study with professional developers to understand how developers set breakpoints. Using the GV, we also analyzed professional developers in two studies and collected data about their debugging sessions. Our observations and the answers to our research questions suggest that sharing and visualizing debugging data can support debugging activities.

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Section: Self-organization and Swarm Intelligencementioning

confidence: 99%

Swarm debugging: The collective intelligence on interactive debugging

Petrillo

Guéhéneuc

Pimenta

et al. 2019

Journal of Systems and Software

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“…However, predators are not omniscient: they decide based on their perceptions of the cost and value of the available options. Predators form these perceptions using their prior experience with similar patches [31] and the cues (signposts in their information environment like links and indicators) that point toward various patches. Of course, predators' perceived values and costs are often inaccurate [32].…”

Section: Background and Related Workmentioning

confidence: 99%

“…This anonymized set-up enabled us to tell our participants that one of the players was an AI agent. (We detail this de- IFT has a long history of revealing useful and usable information functionalities in other information-rich domains, especially web environments (e.g., [33]) and software development environments (e.g., [8,31]). Originally based on classic predator-prey models in the wild, its basic constructs are the predator (information seekers like our participants) seeking prey (information goals) along pathways marked by cues (signposts) in an information environment (such as the StarCraft replay environment).…”

Section: Introductionmentioning

confidence: 99%

Toward Foraging for Understanding of StarCraft Agents

Penney

Dodge

Hilderbrand

et al. 2018

23rd International Conference on Intelligent User Interfaces

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Assessing and understanding intelligent agents is a difficult task for users that lack an AI background. A relatively new area, called "Explainable AI," is emerging to help address this problem, but little is known about how users would forage through information an explanation system might offer. To inform the development of Explainable AI systems, we conducted a formative study -using the lens of Information Foraging Theory -into how experienced users foraged in the domain of StarCraft to assess an agent. Our results showed that participants faced difficult foraging problems. These foraging problems caused participants to entirely miss events that were important to them, reluctantly choose to ignore actions they did not want to ignore, and bear high cognitive, navigation, and information costs to access the information they needed. sign further in the Methodology section.) In addition, the participants had functionality to seek additional information about the replay, such as navigating around the game map, drilling down into production information, pausing, rewinding, fast-forwarding, and so on (Figure 1). However, we wanted a higher level of abstraction than features specific to StarCraft. Specifically, we aimed for (1) applicability to other RTS environments, and (2) connection with other research about humans seeking information. To that end, we turned to Information Foraging Theory (IFT).

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“…To compare the navigation behavior of textual language programmers and visual dataflow language programmers, we compared our data with results reported by four prior studies (i.e., [14,16,36,37]). We focused our comparison on two key navigations traits: (1) how much the programmers navigated and (2) how much the programmers revisited patches.…”

Section: Visual Dataflow Versus Textual Language Programmersmentioning

confidence: 99%

Toward Principles for the Design of Navigation Affordances in Code Editors

Henley

Fleming

Luong

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Design principles are a key tool for creators of interactive systems; however, a cohesive set of principles has yet to emerge for the design of code editors. In this paper, we conducted a between-subjects empirical study comparing the navigation behaviors of 32 professional LabVIEW programmers using two different code-editor interfaces: the ubiquitous tabbed editor and the experimental Patchworks editor. Our analysis focused on how the programmers arranged and navigated among open information patches (i.e., code modules and program output). Key findings of our study included that Patchworks users made significantly fewer click actions per navigation, juxtaposed patches side by side significantly more, and exhibited significantly fewer navigation mistakes than tabbed-editor users. Based on these findings and more, we propose five general principles for the design of effective navigation affordances in code editors.

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To fix or to learn? How production bias affects developers' information foraging during debugging

Cited by 32 publications

References 6 publications

Swarm debugging: The collective intelligence on interactive debugging

Swarm debugging: The collective intelligence on interactive debugging

Toward Foraging for Understanding of StarCraft Agents

Toward Principles for the Design of Navigation Affordances in Code Editors

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