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Philip J. Guo


Publications by Philip J. Guo (bibliography)

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Guo, Philip J., Zimmermann, Thomas, Nagappan, Nachiappan and Murphy, Brendan (2011): "Not my bug!" and other reasons for software bug report reassignments. In: Proceedings of ACM CSCW11 Conference on Computer-Supported Cooperative Work 2011. pp. 395-404. http://dx.doi.org/10.1145/1958824.1958887

Bug reporting/fixing is an important social part of the software development process. The bug-fixing process inherently has strong interpersonal dynamics at play, especially in how to find the optimal person to handle a bug report. Bug report reassignments, which are a common part of the bug-fixing process, have rarely been studied. In this paper, we present a large-scale quantitative and qualitative analysis of the bug reassignment process in the Microsoft Windows Vista operating system project. We quantify social interactions in terms of both useful and harmful reassignments. For instance, we found that reassignments are useful to determine the best person to fix a bug, contrary to the popular opinion that reassignments are always harmful. We categorized five primary reasons for reassignments: finding the root cause, determining ownership, poor bug report quality, hard to determine proper fix, and workload balancing. We then use these findings to make recommendations for the design of more socially-aware bug tracking systems that can overcome some of the inefficiencies we observed in our study.

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Guo, Philip J., Kandel, Sean, Hellerstein, Joseph M. and Heer, Jeffrey (2011): Proactive wrangling: mixed-initiative end-user programming of data transformation scripts. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 65-74. http://dx.doi.org/10.1145/2047196.2047205

Analysts regularly wrangle data into a form suitable for computational tools through a tedious process that delays more substantive analysis. While interactive tools can assist data transformation, analysts must still conceptualize the desired output state, formulate a transformation strategy, and specify complex transforms. We present a model to proactively suggest data transforms which map input data to a relational format expected by analysis tools. To guide search through the space of transforms, we propose a metric that scores tables according to type homogeneity, sparsity and the presence of delimiters. When compared to "ideal" hand-crafted transformations, our model suggests over half of the needed steps; in these cases the top-ranked suggestion is preferred 77% of the time. User study results indicate that suggestions produced by our model can assist analysts' transformation tasks, but that users do not always value proactive assistance, instead preferring to maintain the initiative. We discuss some implications of these results for mixed-initiative interfaces.

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Brandt, Joel, Guo, Philip J., Lewenstein, Joel, Dontcheva, Mira and Klemmer, Scott R. (2009): Two studies of opportunistic programming: interleaving web foraging, learning, and writing code. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 1589-1598. http://doi.acm.org/10.1145/1518701.1518944

This paper investigates the role of online resources in problem solving. We look specifically at how programmers -- an exemplar form of knowledge workers -- opportunistically interleave Web foraging, learning, and writing code. We describe two studies of how programmers use online resources. The first, conducted in the lab, observed participants' Web use while building an online chat room. We found that programmers leverage online resources with a range of intentions: They engage in just-in-time learning of new skills and approaches, clarify and extend their existing knowledge, and remind themselves of details deemed not worth remembering. The results also suggest that queries for different purposes have different styles and durations. Do programmers' queries "in the wild" have the same range of intentions, or is this result an artifact of the particular lab setting? We analyzed a month of queries to an online programming portal, examining the lexical structure, refinements made, and result pages visited. Here we also saw traits that suggest the Web is being used for learning and reminding. These results contribute to a theory of online resource usage in programming, and suggest opportunities for tools to facilitate online knowledge work.

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