Program understanding and the concept assignment problem

Biggerstaff, Ted J.; Mitbander, Bharat G.; Webster, Dallas

doi:10.1145/175290.175300

Cited by 280 publications

(165 citation statements)

References 10 publications

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“…Therefore, searching a code base for concepts, objects, artifacts, etc., using terms that are oblivious to the abbreviation and concatenations actually used can, in the worst case, miss out entirely on the files highly relevant to a given search, and, in the best, result in poor values for the relevancies. While an experienced developer -especially one who is already familiar with the code base -may be able to anticipate the peculiarities of the naming conventions used in a software library, it is easy to imagine how much harder it would be for an inexperienced developer to do the same [7].…”

Section: Introductionmentioning

confidence: 99%

Assisting code search with automatic Query Reformulation for bug localization

Sisman

Kak

2013

2013 10th Working Conference on Mining Software Repositories (MSR)

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Abstract-Source code retrieval plays an important role in many software engineering tasks. However, designing a query that can accurately retrieve the relevant software artifacts can be challenging for developers as it requires a certain level of knowledge and experience regarding the code base. This paper demonstrates how the difficulty of designing a proper query can be alleviated through automatic Query Reformulation (QR) -an under-the-hood operation for reformulating a user's query with no additional input from the user. The proposed QR framework works by enriching a user's search query with certain specific additional terms drawn from the highest-ranked artifacts retrieved in response to the initial query. The important point here is that these additional terms injected into a query are those that are deemed to be "close" to the original query terms in the source code on the basis of positional proximity. This similarity metric is based on the notion that terms that deal with the same concepts in source code are usually proximal to one another in the same files. We demonstrate the superiority of our QR framework in relation to the QR frameworks wellknown in the natural language document retrieval by showing significant improvements in bug localization performance for two large software projects using more than 4,000 queries.

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Section: Introductionmentioning

confidence: 99%

Assisting code search with automatic Query Reformulation for bug localization

Sisman

Kak

2013

2013 10th Working Conference on Mining Software Repositories (MSR)

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“…Note that previous automated reuse component identification systems have employed an algorithmic approach to program understanding (Caldiera and Basili 1991) (as well as focusing on functionally-oriented code The use of informal tokens in this approach goes beyond that of Biggerstaff (Biggerstaff et al 1994). Biggerstaff's approach primarily concentrated on the simple matching of comment keywords.…”

Section: Object-oriented Program Understanding Approachmentioning

confidence: 99%

“…The informal tokens approach is that taken by Biggerstaff (Biggerstaff, Mitbander, and Webster 1994). Biggerstaff argues that a parsing oriented approach based on structural patterns of programming language features is necessary, but not sufficient for solving the general concept assignment problem.…”

Section: Introductionmentioning

confidence: 99%

An approach to program understanding by natural language understanding

1999

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“…Domain-based clustering, as explored by DM-TAO in the DESIRE system (Biggerstaff et al, 1994), focuses on naming mechanisms, by keying in on the patterns of linguistic idioms used in the program, which suggest the manifestations of domain concepts.…”

Section: The Role Of Recognitionmentioning

confidence: 99%

“…For example, slicing (Weiser, 1981) is a widely-used technique for localizing functional components by tracing through data dependencies within the procedural scope. Cluster analysis (Biggerstaff et al, 1994, Hutchens and Basili, 1985, Schwanke, 1991, Schwanke, 1989) is used to group related sections of code, based on the detection of shared uses of global data, control paths, and names. However, clustering techniques can only provide limited assistance by roughly delineating possible locations of functionally cohesive components.…”

Section: Disentangling Unfamiliar Plansmentioning

confidence: 99%

Understanding Interleaved Code

Rugaber

Stirewalt

Wills

Reverse Engineering

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Abstract. Complex programs often contain multiple, interwoven strands of computation, each responsible for accomplishing a distinct goal. The individual strands responsible for each goal are typically delocalized and overlap rather than being composed in a simple linear sequence. We refer to these code fragments as being interleaved. Interleaving may be intentional-for example, in optimizing a program, a programmer might use some intermediate result for several purposes-or it may creep into a program unintentionally, due to patches, quick fixes, or other hasty maintenance practices. To understand this phenomenon, we have looked at a variety of instances of interleaving in actual programs and have distilled characteristic features. This paper presents our characterization of interleaving and the implications it has for tools that detect certain classes of interleaving and extract the individual strands of computation. Our exploration of interleaving has been done in the context of a case study of a corpus of production mathematical software, written in Fortran from the Jet Propulsion Laboratory. This paper also describes our experiences in developing tools to detect specific classes of interleaving in this software, driven by the need to enhance a formal description of this software library's components. The description, in turn aids in the automated component-based synthesis of software using the library.With every leaf a miracle.-Walt Whitman.

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Program understanding and the concept assignment problem

Cited by 280 publications

References 10 publications

Assisting code search with automatic Query Reformulation for bug localization

Assisting code search with automatic Query Reformulation for bug localization

An approach to program understanding by natural language understanding

Understanding Interleaved Code

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