Abstract-Much of software developers' time is spent understanding unfamiliar code. To better understand how developers gain this understanding and how software development environments might be involved, a study was performed in which developers were given an unfamiliar program and asked to work on two debugging tasks and three enhancement tasks for 70 minutes. The study found that developers interleaved three activities. They began by searching for relevant code both manually and using search tools; however, they based their searches on limited and misrepresentative cues in the code, environment, and executing program, often leading to failed searches. When developers found relevant code, they followed its incoming and outgoing dependencies, often returning to it and navigating its other dependencies; while doing so, however, Eclipse's navigational tools caused significant overhead. Developers collected code and other information that they believed would be necessary to edit, duplicate, or otherwise refer to later by encoding it in the interactive state of Eclipse's package explorer, file tabs, and scroll bars. However, developers lost track of relevant code as these interfaces were used for other tasks, and developers were forced to find it again. These issues caused developers to spend, on average, 35 percent of their time performing the mechanics of navigation within and between source files. These observations suggest a new model of program understanding grounded in theories of information foraging and suggest ideas for tools that help developers seek, relate, and collect information in a more effective and explicit manner.

Guessability is essential for symbolic input, in which users enter gestures or keywords to indicate characters or commands, or rely on labels or icons to access features. We present a unified approach to both maximizing and evaluating the guessability of symbolic input. This approach can be used by anyone wishing to design a symbol set with high guessability, or to evaluate the guessability of an existing symbol set. We also present formulae for quantifying guessability and agreement among guesses. An example is offered in which the guessability of the EdgeWrite unistroke alphabet was improved by users from 51.0% to 80.1% without designer intervention. The original and improved alphabets were then tested for their immediate usability with the procedure used by MacKenzie and Zhang (1997). Users entered the original alphabet with 78.8% and 90.2% accuracy after 1 and 5 minutes of learning, respectively. The improved alphabet bettered this to 81.6% and 94.2%. These improved results were competitive with prior results for Graffiti, which were 81.8% and 95.8% for the same measures.

Recently, several innovative tools have found their way into mainstream use in modern development environments. However, most of these tools have focused on creating and modifying code, despite evidence that most of programmers' time is spent understanding code as part of maintenance tasks. If new tools were designed to directly support these maintenance tasks, what types would be most helpful? To find out, a study of expert Java programmers using Eclipse was performed. The study suggests that maintenance work consists of three activities: (1) forming a working set of task-relevant code fragments; (2) navigating the dependencies within this working set; and (3) repairing or creating the necessary code. The study identified several trends in these activities, as well as many opportunities for new tools that could save programmers up to 35% of the time they currently spend on maintenance tasks.

The computer and communication systems that office workers currently use tend to interrupt at inappropriate times or unduly demand attention because they have no way to determine when an interruption is appropriate. Sensor-based statistical models of human interruptibility offer a potential solution to this problem. Prior work to examine such models has primarily reported results related to social engagement, but it seems that task engagement is also important. Using an approach developed in our prior work on sensor-based statistical models of human interruptibility, we examine task engagement by studying programmers working on a realistic programming task. After examining many potential sensors, we implement a system to log low-level input events in a development environment. We then automatically extract features from these low-level event logs and build a statistical model of interruptibility. By correctly identifying situations in which programmers are non-interruptible and minimizing cases where the model incorrectly estimates that a programmer is non-interruptible, we can support a reduction in costly interruptions while still allowing systems to convey notifications in a timely manner.

Three studies of different mobile-device hand postures are presented. The first study measures the performance of postures in Fitts' law tasks using one and two hands, thumbs and index fingers, horizontal and vertical movements, and front-and back-of-device interaction. Results indicate that the index finger performs well on both the front and the back of the device, and that thumb performance on the front of the device is generally worse. Fitts' law models are created and serve as a basis for comparisons. The second study examines the orientation of shapes on the front and back of a mobile device. It shows that participants' expectations of visual feedback for finger movements on the back of a device reverse the direction of their finger movements to favor a ''transparent device'' orientation. The third study examines letter-like gestures made on the front and back of a device. It confirms the performance of the index finger on the front of the device, while showing limitations in the ability for the index finger on the back to perform complex gestures. Taken together, these results provide an empirical foundation upon which new mobile interaction designs can be based. A set of design implications and recommendations are given based directly on the findings presented. r