A visual representation model for software subsystem decomposition

de, Carlos Montes; Carver, Doris L.

doi:10.1109/wcre.1998.723193

Cited by 11 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They use association rule mining for extraction of data cohesive sub-systems by grouping together programs that use the same data files. A very similar approach is that of [30], where the use of a representation model (RM) is discussed to represent the sub-systems identified using the ISA methodology in [29]. Sartipi et al [31] Clustering techniques have also been employed for architecture recovery and software re-modularization [19], [33] - [37].…”

Section: Related Workmentioning

confidence: 99%

Metarule-guided association rule mining for program understanding

et al. 2005

View full text Add to dashboard Cite

Software systems are expected to change over their lifetime in order to remain useful. Understanding a software system that has undergone changes is often difficult due to unavailability of up-to-date documentation. Under these circumstances, source code is the only reliable means of information regarding the system. In this paper, we apply data mining, or more specifically, association rule mining, to the problem of software understanding i.e. given the source files of a software system, we use association rule mining to gain insight about the software. Our purpose is to explore the use of association rule mining for finding interesting associations within the software that can lead to program understanding. To make association rule mining more effective, we place constraints on the mining process in the form of metarules. Metarule-guided mining is carried out to find associations which can be used to identify recurring problems within software systems. We relate metarules to re-engineering patterns which present solutions to these problems. We apply association rule mining to five legacy systems and present results which show how extracted association rules can be helpful in analyzing the structure of a software system and in suggesting modifications to improve the structure. A comparison of the results obtained for the five systems also reveals legacy system characteristics, which can lead to understanding the nature of open source legacy software and its evolution.

show abstract

Section: Related Workmentioning

confidence: 99%

Metarule-guided association rule mining for program understanding

et al. 2005

View full text Add to dashboard Cite

show abstract

“…For example, data mining has been used to decompose a software system into data cohesive subsystems to assist developers with reengineering and maintenance tasks [8,9]. As another example, a related technology, machine learning, has been used in reengineering class hierarchies [22] and generating test cases [5].…”

Section: Related Workmentioning

confidence: 99%

Data mining library reuse patterns in user-selected applications

Michail

14th IEEE International Conference on Automated Software Engineering

View full text Add to dashboard Cite

In this paper, we show how data mining can be used to discover library reuse patterns in user-selected applications. This can be helpful in building and debugging applications that use a particular library by observing how other developers have used that library in their applications.Specifically, we consider the problem of discovering association rules that identify library components that are often reused in combination by application components. For example, such a rule might tell us that application classes that inherit from a particular library class often override certain member functions.By querying and/or browsing such association rules, a developer can discover patterns for reusing library components. We illustrate the approach using our tool, CodeWeb, by demonstrating characteristic ways in which applications reuse components in the ET++ application framework.

show abstract

Concept Representation and Its Role in Artifact Development

Tenoyo

Mursanto

2015

2015 IEEE 39th Annual Computer Software and Applications Conference

View full text Add to dashboard Cite

A visual representation model for software subsystem decomposition

Cited by 11 publications

References 17 publications

Metarule-guided association rule mining for program understanding

Metarule-guided association rule mining for program understanding

Data mining library reuse patterns in user-selected applications

Concept Representation and Its Role in Artifact Development

Contact Info

Product

Resources

About