Analyzing the application of a reverse engineering process to a real situation

Abbattista, Fabio; Fatone, G.M.G.; Lanubile, Filippo; Visaggio, Giuseppe

doi:10.1109/wpc.1994.341251

Cited by 14 publications

(6 citation statements)

References 7 publications

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“…This paper is a revised and extended version of [30] and takes advantage of lessons learned from previous applications of the function extraction to legacy systems [1], [17], [21]. However, the emphasis of this paper is primarily of a theoretical rather than of a practical engineering kind.…”

Section: ----------------mentioning

confidence: 99%

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Extracting reusable functions by flow graph based program slicing

Lanubile¹,

Visaggio²

1997

IIEEE Trans. Software Eng.

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103

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An alternative approach to developing reusable components from scratch is to recover them from existing systems. In this paper, we apply program slicing, a program decomposition method, to the problem of extracting reusable functions from illstructured programs. As with conventional slicing first described by Weiser, a slice is obtained by iteratively solving data flow equations based on a program flow graph. We extend the definition of program slice to a transform slice, one that includes statements which contribute directly or indirectly to transform a set of input variables into a set of output variables. Unlike conventional program slicing, these statements do not include either the statements necessary to get input data or the statements which test the binding conditions of the function. Transform slicing presupposes the knowledge that a function is performed in the code and its partial specification, only in terms of input and output data. Using domain knowledge we discuss how to formulate expectations of the functions implemented in the code. In addition to the input/output parameters of the function, the slicing criterion depends on an initial statement, which is difficult to obtain for large programs. Using the notions of decomposition slice and concept validation we show how to produce a set of candidate functions, which are independent of line numbers but must be evaluated with respect to the expected behavior. Although human interaction is required, the limited size of candidate functions makes this task easier than looking for the last function instruction in the original source code.

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Section: ----------------mentioning

confidence: 99%

“…In [1], the authors propose five variable classification categories: basic conceptual data, derived conceptual data, control data, structure data and redundant data. Both basic and derived conceptual data can be mapped to an entity attribute in the application domain.…”

Section: Expected Functions Elicitationmentioning

confidence: 99%

Extracting reusable functions by flow graph based program slicing

Lanubile¹,

Visaggio²

1997

IIEEE Trans. Software Eng.

Self Cite

103

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“…It is made up of two sub processes: reverse engineering (PRE) and restoration (PREST). Greater detail on these two types of processes can be found in [21,22]. The activities involved in the renewal process P are detailed in Table 1.…”

Section: The Empirical Studymentioning

confidence: 99%

“…Thus the estimation model in use needs to be calibrated on the project parts having the smallest grain of the whole project. Renewal of legacy system projects [13,21,22], as other innovative ones, are not covered by accurate estimation models [10,15]. A renewal process has more characteristics that imply further estimation model requirements.…”

Section: Introductionmentioning

confidence: 99%

Software renewal projects estimation using dynamic calibration

Baldassarre

Caivano

Visaggio

International Conference on Software Maintenance, 2003. ICSM 2003. Proceedings.

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Effort estimation is a long faced problem, but, in spite of the amount of research spent in this field, it still remains an open issue in the software engineering community. This is true especially in the case of renewal of legacy systems, where the current and well established approaches also fail. This paper presents an application of the method named Dynamic Calibration for effort estimation of renewal projects together with its experimental validation. The approach satisfies all the requirements of the estimation models. The results obtained applying dynamic calibration are compared with those obtained with a competitor method: estimation by analogy. The results are shown to be promising although further experimentation on field is needed.

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“…A previous reengineering project, described in [1], had recovered lost requirements and design information, ending with a renovation of the degenerated parts.…”

Section: Experiencing the Iterative Reengineering Modelmentioning

confidence: 99%

Iterative reengineering to compensate for quick-fix maintenance

Lanubile

Visaggio

Proceedings of International Conference on Software Maintenance

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1: IntroductionIn the last few years, the contribution of researchers and practitioners in the reverse engineering field has been very rich of proposals and applications in all the key objectives pointed out in [5]: coping with complexity, generating alternate views, recovering lost information, detecting side effects and analyzing quality, synthesizing higher abstractions, facilitating reuse, and populating a repository or knowledge base. Many research studies and industrial experiences have also been published on reengineering old systems in a new form to decrease maintenance costs, reduce software errors, or upgrade to a new hardware [2]. However, very little has been written regarding what happens in the maintenance process after a software system has been reengineered or simply reverse engineered.Consider the three maintenance process models, proposed by Basili in [3]: quick-fix model, iterativeenhancement model, and full-reuse model. All three models assume that the existing system has a complete and consistent documentation from requirements to code. This assumption can be realistic if we consider the configuration of a system after that a reverse engineering process has been applied. The quick-fix model, shown in Figure 1, starts modifying the existing source code, and then test the new version and modify the system documentation. It represents an abstraction of the common approach to handling software maintenance where all changes stem directly from the implementation abstraction level. The iterative-enhancement model, shown in Figure 2, starts with an analysis of the existing system's documents. The highest level artifact which is affected by the request of change is then modified and the modification is propagated downward through the lower abstraction levels. At each step, the system is redeveloped based on the analysis of the existing system. The full-reuse model, shown in Figure 3, differ from the iterative-enhancement model, because it assumes that there exists a repository of software artifacts from the current and earlier versions of the subject system or similar systems. It starts with the analysis of the requirements for the new system and rebuilds the system by reusing whatever existing document or component is applicable, or developing them when necessary.The full-reuse model promotes the development of reusable artifacts and encourages their reuse also in modification tasks, while the iterative-enhancement model try to accomplish changes by adapting the existing system. Both models differ from the quick-fix approach because the analysis and update of documentation is performed before the modification of the code. This allows maintainer programmers to have a control on the increasing entropy as the system evolve [7].

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Analyzing the application of a reverse engineering process to a real situation

Cited by 14 publications

References 7 publications

Extracting reusable functions by flow graph based program slicing

Extracting reusable functions by flow graph based program slicing

Software renewal projects estimation using dynamic calibration

Iterative reengineering to compensate for quick-fix maintenance

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