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One of the key challenges of developers testing code is determining a test suite's quality -its ability to find faults. The most common approach is to use code coverage as a measure for test suite quality, and diminishing returns in coverage or high absolute coverage as a stopping rule. In testing research, suite quality is often evaluated by a suite's ability to kill mutants (artificially seeded potential faults). Determining which criteria best predict mutation kills is critical to practical estimation of test suite quality. Previous work has only used small sets of programs, and usually compares multiple suites for a single program. Practitioners, however, seldom compare suites -they evaluate one suite. Using suites (both manual and automatically generated) from a large set of real-world open-source projects shows that evaluation results differ from those for suite-comparison: statement (not block, branch, or path) coverage predicts mutation kills best.

In the field of Human-Computer Interaction, provenance refers to the history and genealogy of a document or file. Provenance helps us to understand the evolution and relationships of files; how and when different versions of a document were created, or how different documents in a collection build on each other through copy-paste events. Though methods for tracking provenance and the subsequent use of this meta-data have been proposed and developed into tools, there have been no studies documenting the types and frequency of provenance events in typical computer use. This is knowledge essential for the design of efficient query methods and information displays. We conducted a longitudinal study of knowledge workers at Intel Corporation tracking provenance events in their computer use. We also interviewed knowledge workers to determine the effectiveness of provenance cues for document recall.Our data shows that provenance relationships are common, and provenance cues aid recall.

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