Directed Test Suite Augmentation

As software evolves, engineers use regression testing to evaluate its fitness for release. Such testing typically begins with existing test cases, and many techniques have been proposed for reusing these cost-effectively. After reusing test cases, however, it is also important to consider code or behavior that has not been exercised by existing test cases and generate new test cases to validate these. This process is known as test suite augmentation. In this paper we present a directed test suite augmentation technique, that utilizes results from reuse of existing test cases together with an incremental concolic testing algorithm to augment test suites so that they are coverage-adequate for a modified program. We present results of an empirical study examining the effectiveness of our approach.

Analysis and Transformation of Pipe-like Web Mashups for End User Programmers

Mashups are becoming increasingly popular as end users are able to easily access, manipulate, and compose data from several web sources. To support end users, communities are forming around mashup development environments that facilitate sharing code and knowledge. We have observed, however, that end user mashups tend to suffer from several deficiencies, such as inoperable components or references to invalid data sources, and that those deficiencies are often propagated through the rampant reuse in these end user communities. In this work, we identify and specify ten code smells indicative of deficiencies we observed in a sample of 8,051 pipe-like web mashups developed by thousands of end users in the popular Yahoo! Pipes environment. We show through an empirical study that end users generally prefer pipes that lack those smells, and then present eleven specialized refactorings that we designed to target and remove the smells. Our refactorings reduce the complexity of pipes, increase their abstraction, update broken sources of data and dated components, and standardize pipes to fit the community development patterns. Our assessment on the sample of mashups shows that smells are present in 81% of the pipes, and that the proposed refactorings can reduce that number to 16%, illustrating the potential of refactoring to support thousands of end users developing pipe-like mashups.