At the beginning of a change task, software developers search the source code to locate the places relevant to the task. As previous research and a small exploratory study that we conducted show, developers perform poorly in identifying good search terms and therefore waste a lot of time querying and exploring irrelevant code. To support developers in this step, we present an approach to automatically identify good search terms. Based on existing work and an analysis of change tasks, we derived heuristics, determined their relevancy and used the results to develop our approach. For a preliminary evaluation, we conducted a study with ten developers working on open source change tasks. Our approach was able to identify good search terms for all tasks and outperformed the searches of the participants, illustrating the potential of our approach. In addition, since the used heuristics are solely based on textual features of change tasks, our approach is easy and generally applicable and can leverage much of the existing work on feature location.

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What are software developers doing during a change task? While an answer to this question opens countless opportunities to support developers in their work, only little is known about developers' detailed navigation behavior for realistic change tasks. Most empirical studies on developers performing change tasks are limited to very small code snippets or are limited by the granularity or the detail of the data collected for the study. In our research, we try to overcome these limitations by combining user interaction monitoring with very fine granular eye-tracking data that is automatically linked to the underlying source code entities in the IDE.In a study with 12 professional and 10 student developers working on three change tasks from an open source system, we used our approach to investigate the detailed navigation of developers for realistic change tasks. The results of our study show, amongst others, that the eye-tracking data does indeed capture different aspects than user interaction data and that developers focus on only small parts of methods that are often related by data flow. We discuss our findings and their implications for better developer tool support.

To complete a change task, software developers spend a substantial amount of time navigating code to understand the relevant parts. During this investigation phase, they implicitly build context models of the elements and relations that are relevant to the task. Through an exploratory study with twelve developers completing change tasks in three open source systems, we identified important characteristics of these context models and how they are created. In a second empirical analysis, we further examined our findings on data collected from eighty developers working on a variety of change tasks on open and closed source projects. Our studies uncovered, amongst other results, that code context models are highly connected, structurally and lexically, that developers start tasks using a combination of search and navigation and that code navigation varies substantially across developers. Based on these findings we identify and discuss design requirements to better support developers in the initial creation of code context models. We believe this work represents a substantial step in better understanding developers' code navigation and providing better tool support that will reduce time and effort needed for change tasks.

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