When reading a scholarly paper, scientists oftentimes wish to understand how follow-on work has built on or engages with what they are reading. While a paper itself can only discuss prior work, some scientific search engines can provide a list of all subsequent citing papers; unfortunately, they are undifferentiated and disconnected from the contents of the original reference paper. In this work, we introduce a novel paper reading experience that integrates relevant information about follow-on work directly into a paper, allowing readers to learn about newer papers and see how a paper is discussed by its citing papers in the context of the reference paper. We built a tool, called CiteRead, that implements the following three contributions: 1) automated techniques for selecting important citing papers, building on results from a formative study we conducted, 2) an automated process for localizing commentary provided by citing papers to a place in the reference paper, and 3) an interactive experience that allows readers to seamlessly alternate between the reference paper and information from citing papers (e.g., citation sentences), placed in the margins. Based on a user study with 12 scientists, we found that in comparison to having just a list of citing papers and their citation sentences, the use of CiteRead while reading allows for better comprehension and retention of information about follow-on work.

People spend a significant amount of time trying to make sense of the internet, collecting content from a variety of sources and organizing it to make decisions and achieve their goals. While humans are able to fluidly iterate on collecting and organizing information in their minds, existing tools and approaches introduce significant friction into the process. We introduce Fuse, a browser extension that externalizes users’ working memory by combining low-cost collection with lightweight organization of content in a compact card-based sidebar that is always available. Fuse helps users simultaneously extract key web content and structure it in a lightweight and visual way. We discuss how these affordances help users externalize more of their mental model into the system (e.g., saving, annotating, and structuring items) and support fast reviewing and resumption of task contexts. Our 22-month public deployment and follow-up interviews provide longitudinal insights into the structuring behaviors of real-world users conducting information foraging tasks.

Online research is a frequent and important activity people perform on the Internet, yet current support for this task is basic, fragmented and not well integrated into web browser experiences. Guided by sensemaking theory, we present ForSense, a browser extension for accelerating people’s online research experience. The two primary sources of novelty of ForSense are the integration of multiple stages of online research and providing machine assistance to the user by leveraging recent advances in neural-driven machine reading. We use ForSense as a design probe to explore (1) the benefits of integrating multiple stages of online research, (2) the opportunities to accelerate online research using current advances in machine reading, (3) the opportunities to support online research tasks in the presence of imprecise machine suggestions, and (4) insights about the behaviors people exhibit when performing online research, the pages they visit, and the artifacts they create. Through our design probe, we observe people performing online research tasks, and see that they benefit from ForSense’s integration and machine support for online research. From the information and insights we collected, we derive and share key recommendations for designing and supporting imprecise machine assistance for research tasks.

Online research is a frequent and important activity people perform on the Internet, yet current support for this task is basic, fragmented and not well integrated into web browser experiences. Guided by sensemaking theory, we present ForSense, a browser extension for accelerating people’s online research experience. The two primary sources of novelty of ForSense are the integration of multiple stages of online research and providing machine assistance to the user by leveraging recent advances in neural-driven machine reading. We use ForSense as a design probe to explore (1) the benefits of integrating multiple stages of online research, (2) the opportunities to accelerate online research using current advances in machine reading, and (3) the opportunities to support online research tasks under the presence of imprecise machine suggestions. In our study, we observe people performing online research tasks, and see that they benefit from ForSense’s integration and machine support for online research. From our study, we derive and share key recommendations for designing and supporting imprecise machine assistance for research tasks.

It is widely believed that refactoring improves software quality and developer's productivity by making it easier to maintain and understand software systems. On the other hand, some believe that refactoring has the risk of functionality regression and increased testing cost. This paper investigates the impact of refactoring edits on regression tests using the version history of Java open source projects: (1) Are there adequate regression tests for refactoring in practice? (2) How many of existing regression tests are relevant to refactoring edits and thus need to be re-run for the new version? (3) What proportion of failure-inducing changes are relevant to refactorings? By using a refactoring reconstruction analysis and a change impact analysis in tandem, we investigate the relationship between the types and locations of refactoring edits identified by RefFinder and the affecting changes and affected tests identified by the FaultTracer change impact analysis. The results on three open source projects, JMeter, XMLSecurity, and ANT, show that only 22% of refactored methods and fields are tested by existing regression tests. While refactorings only constitutes 8% of atomic changes, 38% of affected tests are relevant to refactorings. Furthermore, refactorings are involved in almost half of the failed test cases. These results call for new automated regression test augmentation and selection techniques for validating refactoring edits.

Knowing which types of refactoring occurred between two program versions can help programmers better understand code changes. Our survey of refactoring identification techniques found that existing techniques cannot easily identify complex refactorings, such as an replace conditional with polymorphism refactoring, which consist of a set of atomic refactorings. This paper presents REF-FINDER that identifies complex refactorings between two program versions using a template-based refactoring reconstruction approach - REF-FINDER expresses each refactoring type in terms of template logic rules and uses a logic programming engine to infer concrete refactoring instances. It currently supports sixty three refactoring types from Fowler's catalog, showing the most comprehensive coverage among existing techniques. The evaluation using code examples from Fowler's catalog and open source project histories shows that REF-FINDER identifies refactorings with an overall precision of 0.79 and recall of 0.95.

Knowing which parts of a system underwent which types of refactoring between two program versions can help programmers better understand code changes. Though there are a number of techniques that automatically find refactorings from two input program versions, these techniques are inadequate in terms of coverage by handling only a subset of refactoring types---mostly simple rename and move refactorings at the level of classes, methods, and fields. This paper presents a Ref-Finder Eclipse plug-in that automatically identifies both atomic and composite refactorings using a template-based refactoring reconstruction approach---it expresses each refactoring type in terms of template logic queries and uses a logic programming engine to infer concrete refactoring instances. Ref-Finder currently supports sixty three types in the Fowler's catalog, showing the most comprehensive coverage among existing techniques.