An Exploratory Study of How Developers Seek, Relate, and Collect Relevant Information during Software Maintenance Tasks

Ko, Andrew J.; Myers, Brad A.; Coblenz, Michael; Aung, Htet Htet

doi:10.1109/tse.2006.116

Cited by 569 publications

(359 citation statements)

References 42 publications

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“…Search and information seeking behavior of software developers has been studied quite extensively in the past (Sillito et al 2006;Ko et al 2006Ko et al , 2007Singer et al 1997;Murphy et al 2006;Sim et al 1998). However, there have been very few studies on search behavior of developers in Internet-Scale code search engines.…”

Section: Related Workmentioning

confidence: 99%

Analyzing and mining a code search engine usage log

Bajracharya

Lopes

2010

Empir Software Eng

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This paper presents an analysis of a year long usage log of Koders, the first commercially available Internet-Scale code search engine (http://www.koders.com). The usage log comprises about ten million activities from more than three million users. Analysis of the usage data shows that despite of attracting a large number of visitors, Koders has a very sparse usage and that it lacks regular usage from many of its users. When compared to Web search, search behavior in Koders showed many similar patterns. A topic modeling analysis of the usage data shows what topics users of Koders are looking for. Observations on the prevalence of these topics among the users, and observations on how search and download activities vary across topics, lead to the conclusion that users who find code search engines usable are those who already know to a high level of specificity what to look for. This paper also presents a general categorization of these topics that provides insights on the different ways code search engine users express their queries. It identifies various forms of queries in Koders's log and the kinds of results addressed by the queries. It also provides several suggestions for improvements in code search engines based on the analysis of usage, topics, and query forms. The work presented in this paper is the first of its kind that reveals several insights on the usage of an Internet-Scale code search engine.

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Section: Related Workmentioning

confidence: 99%

Analyzing and mining a code search engine usage log

Bajracharya

Lopes

2010

Empir Software Eng

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“…Figure 5 shows how developers spent effort performing each kind of activity. It reveals that developers mostly perform navigating activity (58.72%), reading activity (28.27%), editing [29]. However, while reading code is the first activity in term of effort consuming in Ko et al 's study, our analysis shows that navigating is the most effort consuming activity.…”

Section: D6 Workarounds Needed With Mimec -mentioning

confidence: 57%

Do Code Smells Impact the Effort of Different Maintenance Programming Activities?

Soh

Yamashita

Khomh

et al. 2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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Abstract-Empirical studies have shown so far that code smells have relatively low impact over maintenance effort at file level. We surmise that previous studies have found low effects of code smells because the effort considered is a "sheer-effort" that does not distinguish between the kinds of developers' activities. In our study, we investigate the effects of code smells at the activity level. Examples of activities are: reading, editing, searching, and navigating, which are performed independently over different files during maintenance. We conjecture that structural attributes represented in the form of different code smells do indeed have an effect on the effort for performing certain kinds of activities. To verify this conjecture, we revisit a previous study about the impact of code smell on maintenance effort, using the same dataset, but considering activity effort. Six professional developers were hired to perform three maintenance tasks on four functionally equivalent Java Systems. Each developer performs two maintenance tasks. During maintenance task, we monitor developers' logs. Then, we define an annotation schema to identify developers' activities and assess whether code smells affect different maintenance activities. Results show that different code smells affect differently activity effort. Yet, the size of the changes performed to solve the task impacts the effort of all activities more than code smells and file size. While code smells impact the editing and navigating effort more than file size, the file size impacts the reading and searching activities more than code smells. One major implication of these results is that if code smells indeed affect the effort of certain kinds of activities, it means that their effects are contingent on the type of maintenance task at hand, where some kinds of activities will become more predominant than others.

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“…Some developers may start a task by building and exploring hypotheses (top-down) while some may by reading code (bottom-up) [15]. Some may begin with searching while some with navigation [6,11]. For corrective tasks, the common first step may be recreating the problem; for perfective tasks, the first step may be comprehending the desired behavior.…”

Section: The Problemmentioning

confidence: 99%

An industrial case study of Coman's automated task detection algorithm: What Worked, What Didn't, and Why

Zou

Godfrey

2012

2012 28th IEEE International Conference on Software Maintenance (ICSM)

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Programmer interaction histories are event sequences that have been culled from the use of instrumented IDEs, and can be mined to better understand the work habits of developers. A prerequisite for this analysis is an understanding of where the boundaries between distinct tasks lie. Asking developers to indicate manually when they switch tasks is disruptive to their normal work flow, so researchers have sought ways to infer task boundaries automatically based on the content of the interaction histories. Coman previously reported a fully automated algorithm that achieved 80% accuracy in a lab validation study. In this paper, we evaluate the use of Coman's algorithm within an industrial setting. We found two problems: first, a large number of the tasks identified are in fact only sessions of a larger task; second, the demonstrable eAEects of interruptions are not considered. We argue that the problem of task boundary detection consists of two subproblems: first, detecting task sessions; and second, linking task sessions. Coman's algorithm eAEectively addresses the first, while the second remains unsolved.

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An Exploratory Study of How Developers Seek, Relate, and Collect Relevant Information during Software Maintenance Tasks

Cited by 569 publications

References 42 publications

Analyzing and mining a code search engine usage log

Analyzing and mining a code search engine usage log

Do Code Smells Impact the Effort of Different Maintenance Programming Activities?

An industrial case study of Coman's automated task detection algorithm: What Worked, What Didn't, and Why

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