Automatic white-box test generation is a challenging problem. Many existing tools rely on complex code analyses and heuristics. As a result, structural features of an input program may impact tool effectiveness in ways that tool users and designers may not expect or understand. We develop a technique that uses structural program metrics to predict the test coverage achieved by three automatic test generation tools. We use coverage and structural metrics extracted from 11 software projects to train several decision tree classifiers. Our experiments show that these classifiers can predict high or low coverage with success rates of 82% to 94%.

Software developers must modify their programs to keepup with changing requirements and designs. Often, aconceptually simple change can require numerous editsthat are similar but not identical, leading to errors andomissions. Researchers have designed programming environmentsto address this problem, but most of thesesystems are counter-intuitive and difficult to use.By applying a task-centered design process, we developeda visual tool that allows programmers to makecomplex code transformations in an intuitive manner.This approach uses a representation that aligns wellwith programmers' mental models of programming structures.The visual language combines textual and graphicalelements and is expressive enough to support a broadrange of code-changing tasks. To simplify learning thesystem, its user interface scaffolds construction and executionof transformations. An evaluation with Java programmerssuggests that the interface is intuitive, easyto learn, and effective on a representative editing task.

Implementing large and sweeping changes to software source code can be tedious and error-prone. A conceptually simple change may require a significant code editing effort. Integrating scriptable source-to-source program transformations into development environments can assist developers with this task. We present a developer-oriented interactive source code transformation tool for Java that addresses this need.

Developer testing is of one of the most effective strategies for improving the quality of software, reducing its cost, and accelerating its development. Despite its widely recognized benefits, developer testing is practiced by only a minority of developers. The slow adoption of developer testing is primarily due to the lack of tools that automate some of the more tedious and time-consuming aspects of this practice. Motivated by the need for a solution, and helped and inspired by the research in software test automation, we created a developer testing tool based on software agitation. Software agitation is a testing technique that combines the results of research in test-input generation and dynamic invariant detection. We implemented software agitation in a commercial testing tool called Agitator. This paper gives a high-level overview of software agitation and its implementation in Agitator, focusing on the lessons and challenges of leveraging and applying the results of research to the implementation of a commercial product.