Stack Overflow is commonly used by software developers to help solve problems they face while working on software tasks such as fixing bugs or building new features. Recent research has explored how the content of Stack Overflow posts affects attraction and how the reputation of users attracts more visitors. However, there is very little evidence on the effect that visual attractors and content quantity have on directing gaze toward parts of a post, and which parts hold the attention of a user longer. Moreover, little is known about how these attractors help developers (students and professionals) answer comprehension questions. This paper presents an eye tracking study on thirty developers constrained to reading only Stack Overflow posts while summarizing four open source methods or classes. Results indicate that on average paragraphs and code snippets were fixated upon most often and longest. When ranking pages by number of appearance of code blocks and paragraphs, we found that while the presence of more code blocks did not affect number of fixations, the presence of increasing numbers of plain text paragraphs significantly drove down the fixations on comments. SO posts that were looked at only by students had longer fixation times on code elements within the first ten fixations. We found that 16 developer summaries contained 5 or more meaningful terms from SO posts they viewed. We discuss how our observations of reading behavior could benefit how users structure their posts.

The research proposes four hypotheses that focus on deriving helpful insights from eye patterns, including hidden truths concerning programmer expertise, task context and difficulty. We present results from a study performed in a classroom setting with 17 students, in which we found that novice programmers visit output statements and declarations the same amount as the rest of the program they are presented other than control flow block headers.

Safety-critical applications often use dependability cases to validate that specified properties are invariant, or to demonstrate a counter example showing how that property might be violated. However, most dependability cases are written with a single product in mind. At the same time, software product lines (families of related software products) have been studied with the goal of modeling variability and commonality, and building family based techniques for both analysis and testing. However, there has been little work on building an end to end dependability case for a software product line (where a property is modeled, a counter example is found and then validated as a true positive via testing), and none that we know of in an emerging safety-critical domain, that of robotic surgery. In this paper, we study a family of surgical robots, that combine hardware and software, and are highly configurable, representing over 1300 unique robots. At the same time, they are considered safety-critical and should have associated dependability cases. We perform a case study to understand how we can bring together lightweight formal analysis, feature modeling, and testing to provide an end to end pipeline to find potential violations of important safety properties. In the process, we learned that there are some interesting and open challenges for the research community, which if solved will lead towards more dependable safety-critical cyber-physical systems.

Most automated testing techniques for graphical user interfaces (GUIs) produce test cases that are only concerned with covering the elements (widgets, menus, etc.) on the interface, or the underlying program code, with little consideration of test case semantics. This is effective for functional testing where the aim is to find as many faults as possible. However, when one wants to mimic a real user for evaluating usability, or when it is necessary to extensively test important end-user tasks of a system, or to generate examples of how to use an interface, this generation approach fails. Capture and replay techniques can be used, however there are often multiple ways to achieve a particular goal, and capturing all of these is usually too time consuming and unrealistic. Prior work on human performance regression testing introduced a constraint based method to filter test cases created by a functional test case generator, however that work did not capture the specifications, or directly generate only the required tests and considered only a single type of test goal. In this paper we present EventFlowSlicer, a tool that allows the GUI tester to specify and generate all realistic test cases relevant to achieve a stated goal. The user first captures relevant events on the interface, then adds constraints to provide restrictions on the task. An event flow graph is extracted containing only the widgets of interest for that goal. Next all test cases are generated for edges in the graph which respect the constraints. The test cases can then be replayed using a modified version of GUITAR. A video demonstration of EventFlowSlicer can be found at https://youtu.be/hw7WYz8WYVU.