Searching a large document collection to learn about a broad subject involves the iterative process of figuring out what to ask, filtering the results, identifying useful documents, and deciding when one has covered e...
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Searching a large document collection to learn about a broad subject involves the iterative process of figuring out what to ask, filtering the results, identifying useful documents, and deciding when one has covered enough material to stop searching. We are calling this activity "discoverage," discovery of relevant material and trackingcoverage of that material. We built a visual analytic tool called Footprints that uses multiple coordinated visualizations to help users navigate through the discoverage process. To support discovery, Footprints displays topics extracted from documents that provide an overview of the search space and are used to construct searches visuospatially. Footprints allows users to triage their search results by assigning a status to each document (To Read, Read, Useful), and those status markings are shown on interactive histograms depicting the user's coverage through the documents across dates, sources, and topics. coverage histograms help users notice biases in their search and fill any gaps in their analytic process. To create Footprints, we used a highly iterative, user-centered approach in which we conducted many evaluations during both the design and implementation stages and continually modified the design in response to feedback.
Data-flow testing (DFT) is a family of testing strategies designed to verify the interactions between each program variable's definition and its uses. Such a test objective of interest is referred to as a def-use ...
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Data-flow testing (DFT) is a family of testing strategies designed to verify the interactions between each program variable's definition and its uses. Such a test objective of interest is referred to as a def-use pair. DFT selects test data with respect to various test adequacy criteria (i.e., data-flow coverage criteria) to exercise each pair. The original conception of DFT was introduced by Herman in 1976. Since then, a number of studies have been conducted, both theoretically and empirically, to analyze DFT's complexity and effectiveness. In the past four decades, DFT has been continuously concerned, and various approaches from different aspects are proposed to pursue automatic and efficient data-flow testing. This survey presents a detailed overview of data-flow testing, including challenges and approaches in enforcing and automating it: (1) it introduces the data-flow analysis techniques that are used to identify def-use pairs;(2) it classifies and discusses techniques for data-flow-based test data generation, such as search-based testing, random testing, collateral-coverage- based testing, symbolic-execution-based testing, and model-checking-based testing;(3) it discusses techniques for tracking data-flow coverage;(4) it presents several DFT applications, including software fault localization, web security testing, and specification consistency checking;and (5) it summarizes recent advances and discusses future research directions toward more practical data-flow testing.
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