During maintenance, developers often need to understand the purpose of a test. One of the most potentially useful sources of information for understanding a test is its name. Ideally, test names are descriptive in that they accurately summarize both the scenario and the expected outcome of the test. Despite the benefits of being descriptive, test names often fall short of this goal. In this paper we present a new approach for automatically generating descriptive names for existing test bodies. Using a combination of natural-language program analysis and text generation, the technique creates names that summarize the test's scenario and the expected outcome. The results of our evaluation show that, (1) compared to alternative approaches, the names generated by our technique are significantly more similar to human-generated names and are nearly always preferred by developers, (2) the names generated by our technique are preferred over or are equivalent to the original test names in 83% of cases, and (3) our technique is several orders of magnitude faster than manually writing test names.

Existing specification-based testing techniques require specifications that either do not exist or are too difficult to create. As a result, they often fall short of their goal of helping developers test expected behaviors. In this paper we present a novel, natural language-based approach that exploits the descriptive nature of test names to generate test templates. Similar to how modern IDEs simplify development by providing templates for common constructs such as loops, test templates can save time and lower the cognitive barrier for writing tests. The results of our evaluation show that the approach is feasible: despite the difficulty of the task, when test names contain a sufficient amount of information, the approach's accuracy is over 80% when parsing the relevant information from the test name and generating the template.

Unintended information leakage is one of the most common and severe problems facing modern applications. To help developers detect information leaks before they can be leveraged by attackers, we present a new static analysis-based technique for detecting a specific type of information leak: information leaks via exceptions. Because it focuses on a specific type of leak, the technique is able to be efficient, effective, and easy to use, qualities that are often lacking in more general techniques. We implemented our technique in a prototype tool, UDLD, and performed an extensive empirical evaluation using 19 real web applications. The results of the evaluation show that UDLD is both efficient and effective at detecting unsafe information leaks via exceptions; for the subjects that we considered, UDLD is the fastest among several alternative tools. Moreover, it reported more true leaks than existing state-of-the-art tools with no known false negatives and no false positives.