A memory-based approach to recognizing programming plans

Quilici, Alex

doi:10.1145/175290.175301

Cited by 89 publications

(27 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Representation replacing codes with abstract model such as event for Quicili [16,17] represents syntax tree entity. Abstraction was needed for recognition because it will simplify searching area for program representation.…”

Section: Related Workmentioning

confidence: 99%

Implementation of CONCEIVER++: An Object-Oriented Program Understanding System

Sani¹,

Zin²,

Idris³

2009

J. of Computer Science

View full text Add to dashboard Cite

Problem statement: Understanding on computer program is a complex cognitive activity. It is ability and also a difficult task especially for novice programmer. The object-oriented languages has widely used in education and industry recently. In programming it is important to have such software which can aid programmers or students to code the program. But, available program understanding systems using the plan based approach usually are developed for non-object-oriented programming languages. Reviewed from the available system also showed that none of the plan formalisms used is for an object-oriented language. Specifically, problem arises when the existing system is not usable for teaching programming purposes. Program understanding system with plan for object-oriented does not exist was the main reason why this research is being carried out. Approach: Method used on developed the program understanding system named CONCEIVER++ is Unified Approach (UA). The process involved from UA for developing and testing the system is iterative development and continuous testing. The process must be iterate and reiterate until satisfied with the system. In order to test the quality assurance of the system is by choosing the black box testing strategies. Results: The object-oriented program understanding system has been successfully implemented. The implementation is tested with an example of Java programming code. The binary search tree for control flow graph and linked list for plan has been generated. Results of understanding the meaning or semantic of the program codes also has been produced. The black box testing had shows that all statements of line of code of the example program have been recognized and the correctness output has been checked. Conclusion: The understanding module of CONCEIVER++, which are code/CFG processor, plan processor and recognition engine has been tested. All line of codes (or nodes) has been recognized and got correct meaning using the developed module.

show abstract

Section: Related Workmentioning

confidence: 99%

Implementation of CONCEIVER++: An Object-Oriented Program Understanding System

Sani¹,

Zin²,

Idris³

2009

J. of Computer Science

View full text Add to dashboard Cite

show abstract

“…Note that a plan is not necessarily stereotypical or used repeatedly; it may be novel or idiosyncratic. Following Waters, 1990, Selfridge et al, 1993) , we reserve the term cliché for a plan that represents a standard, stereotypical form, which can be detected by recognition techniques, such as (Hartman, 1991, Letovsky, 1988, Kozaczynski and Ning, 1994, Quilici, 1994, Rich and Wills, 1990, Wills, 1992. .…”

Section: Npedlnmentioning

confidence: 99%

“…When what is interleaved is familiar (i.e., stereotypical, frequently used plans), cliché recognition (e.g., (Hartman, 1991, Johnson, 1986, Kozaczynski and Ning, 1994, Letovsky, 1988, Quilici, 1994, Rich and Wills, 1990, Wills, 1992 ) is a useful detection mechanism. In fact, most recognition systems deal explicitly with the recognition of clichés that are interleaved in specific ways with unrecognizable code or other clichés.…”

Section: The Role Of Recognitionmentioning

confidence: 99%

Understanding interleaved code

Rugaber

Stirewalt

Wills

1996

Automated Software Engineering

View full text Add to dashboard Cite

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.

show abstract

“…In this direction, an explicit library of programming plan templates and concepts is constructed, and various top-down and bottom-up search strategies are utilized to implement the mapping process. Notable examples are Quilici (1994), Kozaczynski and Ning (1994), Rich and Waters (1990) and Wills (1990Wills ( , 1992. To some extent, all are aimed at improving the effectiveness of the mapping process through heuristic knowledge.…”

Section: Introductionmentioning

confidence: 99%

Program understanding as constraint satisfaction

Woods¹,

Yang²

Proceedings of 2nd Working Conference on Reverse Engineering

View full text Add to dashboard Cite

Abstract. The process of understanding a source code in a high-level programming language involves complex computation. Given a piece of legacy code and a library of program plan templates, understanding the code corresponds to building mappings from parts of the source code to particular program plans. These mappings could be used to assist an expert in reverse engineering legacy code, to facilitate software reuse, or to assist in the translation of the source into another programming language. In this paper we present a model of program understanding using constraint satisfaction. Within this model we intelligently compose a partial global picture of the source program code by transforming knowledge about the problem domain and the program itself into sets of constraints. We then systematically study different search algorithms and empirically evaluate their performance. One advantage of the constraint satisfaction model is its generality; many previous attempts in program understanding could now be cast under the same spectrum of heuristics, and thus be readily compared. Another advantage is the improvement in search efficiency using various heuristic techniques in constraint satisfaction.

show abstract

A memory-based approach to recognizing programming plans

Cited by 89 publications

References 7 publications

Implementation of CONCEIVER++: An Object-Oriented Program Understanding System

Implementation of CONCEIVER++: An Object-Oriented Program Understanding System

Understanding interleaved code

Program understanding as constraint satisfaction

Contact Info

Product

Resources

About