A wide range of techniques for supporting software maintenance tasks rely on representations of program control flow. The accuracy of these representations can be important to the effectiveness and efficiency of these techniques. The Java programming language has introduced structured exception handling features that complicate the task of representing control flow. Previous work has attempted to address these complications by using type inference algorithms to analyze the control flow effects of exceptions, but to date, there has been no study of whether the use of these algorithms is justified. In this paper we report results of an empirical study addressing this issue. We find that type inference algorithms can lead to more accurate representations of control flow, but this improvement does not necessarily translate into benefits for maintenance techniques that use them. It follows that type inference algorithms should not just automatically be applied; rather, the tradeoffs of applying them must first be assessed with respect to particular maintenance techniques and workloads.

Analyzing a program run can provide important insights about its correctness. Dynamic analysis of complex correctness properties, however, usually results in significant run-time overhead and, consequently, it is rarely used in practice. In this paper, we present an approach for exploiting properties of stateful program specifications to reduce the cost of their dynamic analysis. With our approach, analysis results are guaranteed to be identical to those of a traditional expensive dynamic analyses, while analysis cost is very low - between 23% and 33% more than the un-instrumented program for the analyses we studied. We describe the principles behind our adaptive online program analysis technique, extentions to our Java run-time analysis framework that support such analyses, and report on the performance and capabilities of two different families of adaptive online program analyses.

Test case prioritization provides a way to run test cases with the highest priority earliest. Numerous empirical studies have shown that prioritization can improve a test suite's rate of fault detection, but the extent to which these results generalize is an open question because the studies have all focused on a single procedural language, C, and a few specific types of test suites, in particular, Java and the JUnit testing framework are being used extensively in practice, and the effectiveness of prioritization techniques on Java systems tested under JUnit has not been investigated. We have therefore designed and performed a controlled experiment examining whether test case prioritization can be effective on Java programs tested under JUnit, and comparing the results to those achieved in earlier studies. Our analyses show that test case prioritization can significantly improve the rate of fault detection of JUnit test suites, but also reveal differences with respect to previous studies that can be related to the language and testing paradigm.