Hipikat: recommending pertinent software development artifacts

By Davor Cubranic, Gail C. Murphy. 25th International Conference on Software Engineering, 2003. Proceedings., 2003

403 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/fc041feb5271efca959d0f54722338d419330cf0

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Citations in the corpus

Classifying Software Changes: Clean or Buggy? (2008, methodology): “Two approaches are used for this step: searching for keywords such as “Fixed” or “Bug” [32] and searching for references to bug reports like “#42233” [6], [7], [47].”
Strathcona example recommendation tool (2005, methodology): “Another tool that automatically builds queries to send to the repository is the Hipikat tool [1].”
CoCoMIC: Code Completion by Jointly Modeling In-file and Cross-file Context (2022, neutral): “Earlier works (Henninger, 1991; Rosson and Carroll, 1996; Michail, 2001; Ye et al., 2000; Ye and Fischer, 2002; Cubranic and Murphy, 2003; Inoue et al., 2003; Hill and Rideout, 2004; Holmes and Murphy, 2005) in software engineering literature focused on developing tools to extract information from s”
Predicting Defects for Eclipse (2007, background): “With this knowledge, we can search for properties of the program or its development process that commonly correlate with defect density; in other words, once we can measure the effect, we can search for its causes.”
Feature location in source code: a taxonomy and survey (2013, methodology): “Hipikat [20, 98] is a feature location tool that also makes use of archival information for feature location, but instead of identifying candidate program elements, Hipikat recommends artifacts from a project’s archives such as online documentation, versions, bugs, or communications.”
Global Software Engineering: The Future of Socio-technical Coordination (2007, background): “Typical software development tools tend to create a potentially very rich “project memory” [17] with versioned files, change histories, and documents, some small fraction of which are highly relevant to current work.”
Using structural context to recommend source code examples (2005, neutral): “The Hipikat tool [2] can recommend relevant development artifacts from a project's history to a developer.”
Automatic Identification of Bug-Introducing Changes (2006, background): “To do so, one examines change log messages in two ways: searching for keywords such as "Fixed" or "Bug" [12] and searching for references to bug reports like " #42233 " [2] [4] [16].”
When do changes induce fixes? (2005, methodology): “3Čubranić and Murphy already applied this as a standalone technique to relate bugs to transactions in their HIPIKAT tool [4].”
Mining version histories to guide software changes (2005, background): “In earlier work (Section 8), researchers have used history data to understand programs and their evolution [3], to detect evolutionary coupling between files [10] or classes [5], or to support navigation in the source code [8].”
Mining metrics to predict component failures (2006, background): “The ke y idea is that one can map problems (in the bug database) to fixes (in the version database) and thus to those locations in th e code that caused the problem [9, 12, 20].”
It's not a bug, it's a feature: How misclassification impacts bug prediction (2013, background): “We also thank Brendan Murphy (Microsoft) and Christian Holler (Mozilla) for insightful discussions and feedback.”
Predicting source code changes by mining change history (2004, background): “…is to find frequent patterns by a depthfirst approach of recursively mining a pattern of increasing cardinality from the data structure FP-tree that encodes D, as opposed to a breadth-first approach of finding all patterns of the same cardinality before finding patterns of a larger cardinality.”
An information retrieval approach to concept location in source code (2004, background): “Hipikat [7] recommends the user relevant software artifacts when adding new features to Eclipse.”
An Exploratory Study of How Developers Seek, Relate, and Collect Relevant Information during Software Maintenance Tasks (2006, methodology): “Tools such as Hipikat have tried to automatically find potentially relevant code for developers to inspect [13].”