Robert S. Roos

TimeAware test suite prioritization

Regression test prioritization is often performed in a time constrained execution environment in which testing only occurs for a fixed time period. For example, many organizations rely upon nightly building and regression testing of their applications every time source code changes are committed to a version control repository. This paper presents a regression test prioritization technique that uses a genetic algorithm to reorder test suites in light of testing time constraints. Experiment results indicate that our prioritization approach frequently yields higher average percentage of faults detected (APFD) values, for two case study applications, when basic block level coverage is used instead of method level coverage. The experiments also reveal fundamental trade offs in the performance of time-aware prioritization. This paper shows that our prioritization technique is appropriate for many regression testing environments and explains how the baseline approach can be extended to operate in additional time constrained testing circumstances.

Empirically studying the role of selection operators duringsearch-based test suite prioritization

Regression test suite prioritization techniques reorder test cases so that, on average, more faults will be revealed earlier in the test suites execution than would otherwise be possible. This paper presents a genetic algorithm-based test prioritization method that employs a wide variety of mutation, crossover, selection, and transformation operators to reorder a test suite. Leveraging statistical analysis techniques, such as tree model construction through binary recursive partitioning and kernel density estimation, the papers empirical results highlight the unique role that the selection operators play in identifying an effective ordering of a test suite. The study also reveals that, while truncation selection consistently outperformed the tournament and roulette operators in terms of test suite effectiveness, increasing selection pressure consistently produces the best results within each class of operator. After further explicating the relationship between selection intensity, termination condition, fitness landscape, and the quality of the resulting test suite, this paper demonstrates that the genetic algorithm-based prioritizer is superior to random search and hill climbing and thus suitable for many regression testing environments.

Efficient Learning of Real Time One-Counter Automata

We present an efficient learning algorithm for languages accepted by deterministic real time one-counter automata (ROCA). The learning algorithm works by first learning an initial segment, B n , of the infinite state machine that accepts the unknown language and then decomposing it into a complete control structure and a partial counter. A new, efficient ROCA decomposition algorithm, which will be presented in detail, allows this result. The decomposition algorithm works in time O(n2log(n)) where nc is the number of states of B n for some language-dependent constant c. If Angluins algorithm for learning regular languages is used to learn B n and the complexity of this step is h(n, m), where m is the length of the longest counterexample necessary for Angluins algorithm, the complexity of our algorithm is O(h(n,m) + n2log(n)).

The networks course: old problems, new solutions

New approaches and techniques for teaching the undergraduate level course in networks and data communications will be discussed. Many of these ideas were presented at the 1998 Workshop on Networking sponsored by NSF and Michigan State Universitys Department of Computer Science and Engineering.