Understanding interleaved code

Rugaber, Spencer; Stirewalt, Kurt; Wills, Linda M.

doi:10.1007/bf00126959

Cited by 12 publications

(2 citation statements)

References 27 publications

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“…It is a difficult problem because the boundaries between concepts can be confused and fuzzy to the point where two concepts may interleave. Interleaving has been addressed in algorithmic understanders using data and control flow information (see [RUGA96]). It presents a more difficult problem to plausible reasoning understanders, such as HB-CA, where this kind of information is not used.…”

Section: The Segmentation Problemmentioning

confidence: 99%

Hypothesis-based concept assignment to support software maintenance

Gold

Proceedings IEEE International Conference on Software Maintenance. ICSM 2001

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source• a link is made to the metadata record in Durham E-Theses• the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders. Ph.D. Thesis Research Institute in Software Evolution Department of Computer Science University of Durham 2000 AbstractSoftware comprehension is one of the most expensive activities in software maintenance and many tools have been developed to help the maintainer reduce the time and cost of the task. Of the numerous tools and methods available, one group has received relatively little attention: those using plausible reasoning to address the concept assignment problem. This problem is defined as the process of assigning descriptive terms to their implementation in source code, the terms being nominated by a user and usually relating to computational intent. It has two major research issues:• Segmentation: finding the location and extent of concepts in the source code.• Concept Binding: determining which concepts are implemented at these locations.This thesis presents a new concept assignment method: Hypothesis-Based Concept Assignment (HB-CA). A framework for the activity of software comprehension is defined using elements of psychological theory and software tools. In this context, HB-CA is presented as a successful concept assignment method for COBOL II, employing a simple knowledge base (the library) to model concepts, source code indicators, and inter-concept relationships. The library and source code are used to generate hypotheses on which segmentation and concept binding are performed.A two-part evaluation is presented using a prototype implementation of HB-CA.The first part shows that HB-CA has linear computational growth in the length of program under analysis. Other characteristics addressed include HB-CA's scalability, its applicability to other languages, the contribution made by different information sources, domain independence, representational power, and guidelines for the content of the library. The first part concludes by comparing the method and implementation to cognitive requirements for software comprehension tools.The second part considers applications of HB-CA in software maintenance. Five areas for potential cost reduction are identified: business-rule ripple analysis, code ripple analysis, module selection, software reuse, and software module comprehension.

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Section: The Segmentation Problemmentioning

confidence: 99%

Hypothesis-based concept assignment to support software maintenance

Gold

Proceedings IEEE International Conference on Software Maintenance. ICSM 2001

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“…• Legacy programs often have large procedures that contain multiple strands of distinct computations; such strands often occur one after the other within the procedure, but it is not uncommon for them to be interleaved with each other [18,21]. Extracting the individual strands into separate procedures aids program comprehension as each new procedure performs a single cohesive computation [8].…”

Section: Introductionmentioning

confidence: 99%