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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.

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The paper presents a novel approach for recovering software traceability links from developers' eye gazes. An eye tracker is used to capture eye gazes while developers perform software maintenance tasks within the Eclipse IDE. An algorithm is presented that establishes a set of traceability links from the eye-gaze data of several developer sessions. A preliminary study assesses the feasibility and validity of the approach. The links generated by the approach were validated by another set of developers. Results indicate that our algorithm achieves strong recall when developers accurately perform bug-localization tasks.

The more we know about software developers' detailed navigation behavior for change tasks, the better we are able to provide effective tool support. Currently, most empirical studies on developers performing change tasks are, however, limited to very small code snippets or limited by the granularity and detail of the data collected on developer's navigation behavior. In our research, we extend this work by combining user interaction monitoring to gather interaction context-the code elements a developer selects and edits-with eye-tracking to gather more detailed and fine-granular gaze context-code elements a developer looked at. In a study with 12 professional and 10 student developers we gathered interaction and gaze contexts from participants working on three change tasks of an open source system. Based on an analysis of the data we found, amongst other results, that gaze context captures different aspects than interaction context and that developers only read small portions of code elements. We further explore the potential of the more detailed and fine-granular data by examining the use of the captured change task context to predict perceived task difficulty and to provide better and more fine-grained navigation recommendations. We discuss our findings and their implications for better tool support.