An empirical investigation into the impact of refactoring on regression testing

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 a half of failed test cases. These results call for new automated regression test augmentation and selection techniques for validating refactoring edits.Electrical and Computer Engineerin

The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces

Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature grows, the need for new technology to support the reading process grows. In contrast to the process of finding papers, which has been transformed by Internet technology, the experience of reading research papers has changed little in decades. The PDF format for sharing research papers is widely used due to its portability, but it has significant downsides including: static content, poor accessibility for low-vision readers, and difficulty reading on mobile devices. This paper explores the question "Can recent advances in AI and HCI power intelligent, interactive, and accessible reading interfaces -- even for legacy PDFs?" We describe the Semantic Reader Project, a collaborative effort across multiple institutions to explore automatic creation of dynamic reading interfaces for research papers. Through this project, we've developed ten research prototype interfaces and conducted usability studies with more than 300 participants and real-world users showing improved reading experiences for scholars. We've also released a production reading interface for research papers that will incorporate the best features as they mature. We structure this paper around challenges scholars and the public face when reading research papers -- Discovery, Efficiency, Comprehension, Synthesis, and Accessibility -- and present an overview of our progress and remaining open challenges

An Empirical Investigation into the Impact of Refactoring on Regression Testing

Abstract—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. Keywords-software evolution, refactoring, empirical study, regression testing I

Learning depends on knowledge: The benefits of retrieval practice vary for facts and skills

Retrieval practice of information through testing has been shown to improve learning. So has studying examples. In this paper, we address inconsistencies in the literature concerning which of these two approaches is best. We test the hypothesis that learning depends on what is being learned; whereas practice emphasizes memorization, studying examples allows for selectivity of encoding, resulting in different information being learned. Accordingly, we predicted that practice will improve learning in situations that emphasize memorization (such as learning facts or simple associations), whereas studying examples will improve learning in situations where there are multiple pieces of information available and selectivity is necessary (such as when learning skills or procedures). We report evidence from a laboratory study using naturalistic materials showing results consistent with these predictions

Template-based reconstruction of complex refactorings

Abstract—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 templatebased 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. I