Author
Radu Marinescu
Other affiliations: Politehnica University of Bucharest
Bio: Radu Marinescu is an academic researcher from Politehnica University of Timișoara. The author has contributed to research in topics: Software system & Object-oriented design. The author has an hindex of 20, co-authored 40 publications receiving 2766 citations. Previous affiliations of Radu Marinescu include Politehnica University of Bucharest.
Papers
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11 Sep 2004
TL;DR: This work proposes a novel mechanism - called detection strategy - for formulating metrics-based rules that capture deviations from good design principles and heuristics, and defined such detection strategies for capturing around ten important flaws of object-oriented design found in the literature.
Abstract: In order to support the maintenance of an object-oriented software system, the quality of its design must be evaluated using adequate quantification means. In spite of the current extensive use of metrics, if used in isolation metrics are oftentimes too fine grained to quantify comprehensively an investigated design aspect (e.g., distribution of system's intelligence among classes). To help developers and maintainers detect and localize design problems in a system, we propose a novel mechanism - called detection strategy - for formulating metrics-based rules that capture deviations from good design principles and heuristics. Using detection strategies an engineer can directly localize classes or methods affected by a particular design flaw (e.g., God Class), rather than having to infer the real design problem from a large set of abnormal metric values. We have defined such detection strategies for capturing around ten important flaws of object-oriented design found in the literature and validated the approach experimentally on multiple large-scale case-studies.
591 citations
Book•
01 Nov 2005
TL;DR: This is a book that will show you even new to old thing, and when you are really dying of object oriented metrics in practice, just pick this book; it will be right for you.
Abstract: It's coming again, the new collection that this site has. To complete your curiosity, we offer the favorite object oriented metrics in practice book as the choice today. This is a book that will show you even new to old thing. Forget it; it will be right for you. Well, when you are really dying of object oriented metrics in practice, just pick it. You know, this book is always making the fans to be dizzy if not to find.
516 citations
Book•
03 Aug 2006
TL;DR: A novel metrics-based approach for detecting design problems in object-oriented software and introduces an important suite of detection strategies for the identification of different well-known design flaws as well as some rarely mentioned ones.
Abstract: Presents a novel metrics-based approach for detecting design problems in object-oriented software. Introduces an important suite of detection strategies for the identification of different well-known design flaws as well as some rarely mentioned ones.
389 citations
25 Sep 2005
TL;DR: The dissertation proposes a novel type of quality model, called factor-strategy, which relates explicitly the quality of a design to its conformance with a set of essential principles, rules and heuristics, which are quantified using detection strategies.
Abstract: In order to support the maintenance of object-oriented software systems, the quality of their design must be evaluated using adequate quantification means. In spite of the current extensive use of metrics, if used in isolation, metrics are oftentimes too fine grained to quantify comprehensively an investigated aspect of the design. To help the software engineer detect and localize design problems, the novel detection strategy mechanism is defined so that deviations from good-design principles and heuristics are quantised inform of metrics-based rules. Using detection strategies an engineer can directly localize classes or methods affected by a particular design flaw (e.g. God Class), rather than having to infer the real design problem from a large set of abnormal metric values. In order to reach the ultimate goal of bridging the gap between qualitative and quantitative statements about design, the dissertation proposes a novel type of quality model, called factor-strategy. In contrast to traditional quality models that express the goodness of design in terms of a set of metrics, this novel model relates explicitly the quality of a design to its conformance with a set of essential principles, rules and heuristics, which are quantified using detection strategies.
200 citations
24 Mar 2004
TL;DR: This work applies its approach on a large scale case study and shows how it improves the accuracy of the detection of god classes and data classes, and additionally how it adds valuable semantical information about the evolution of flawed design structures.
Abstract: As systems evolve and their structure decays, maintainers need accurate and automatic identification of the design problems. Current approaches for automatic detection of design problems are not accurate enough because they analyze only a single version of a system and consequently they miss essential information as design problems appear and evolve over time. Our approach is to use the historical information of the suspected flawed structure to increase the accuracy of the automatic problem detection. Our means is to define measurements which summarize how persistent the problem was and how much maintenance effort was spent on the suspected structure. We apply our approach on a large scale case study and show how it improves the accuracy of the detection of god classes and data classes, and additionally how it adds valuable semantical information about the evolution of flawed design structures.
171 citations
Cited by
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TL;DR: A qualitative comparison and evaluation of the current state-of-the-art in clone detection techniques and tools is provided, and a taxonomy of editing scenarios that produce different clone types and a qualitative evaluation of current clone detectors are evaluated.
989 citations
01 Jan 2007
TL;DR: The state of the art in clone detection research is surveyed, the clone terms commonly used in the literature are described along with their corresponding mappings to the commonly used clone types and several open problems related to clone detectionResearch are pointed out.
Abstract: Code duplication or copying a code fragment and then reuse by pasting with or without any modiflcations is a well known code smell in software maintenance. Several studies show that about 5% to 20% of a software systems can contain duplicated code, which is basically the results of copying existing code fragments and using then by pasting with or without minor modiflcations. One of the major shortcomings of such duplicated fragments is that if a bug is detected in a code fragment, all the other fragments similar to it should be investigated to check the possible existence of the same bug in the similar fragments. Refactoring of the duplicated code is another prime issue in software maintenance although several studies claim that refactoring of certain clones are not desirable and there is a risk of removing them. However, it is also widely agreed that clones should at least be detected. In this paper, we survey the state of the art in clone detection research. First, we describe the clone terms commonly used in the literature along with their corresponding mappings to the commonly used clone types. Second, we provide a review of the existing clone taxonomies, detection approaches and experimental evaluations of clone detection tools. Applications of clone detection research to other domains of software engineering and in the same time how other domain can assist clone detection research have also been pointed out. Finally, this paper concludes by pointing out several open problems related to clone detection research.
736 citations
TL;DR: DETEX is proposed, a method that embodies and defines all the steps necessary for the specification and detection of code and design smells, and a detection technique that instantiates this method, and an empirical validation in terms of precision and recall of DETEX.
Abstract: Code and design smells are poor solutions to recurring implementation and design problems. They may hinder the evolution of a system by making it hard for software engineers to carry out changes. We propose three contributions to the research field related to code and design smells: (1) DECOR, a method that embodies and defines all the steps necessary for the specification and detection of code and design smells, (2) DETEX, a detection technique that instantiates this method, and (3) an empirical validation in terms of precision and recall of DETEX. The originality of DETEX stems from the ability for software engineers to specify smells at a high level of abstraction using a consistent vocabulary and domain-specific language for automatically generating detection algorithms. Using DETEX, we specify four well-known design smells: the antipatterns Blob, Functional Decomposition, Spaghetti Code, and Swiss Army Knife, and their 15 underlying code smells, and we automatically generate their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall on XERCES v2.7.0, and discuss the precision of these algorithms on 11 open-source systems.
710 citations
11 Sep 2004
TL;DR: This work proposes a novel mechanism - called detection strategy - for formulating metrics-based rules that capture deviations from good design principles and heuristics, and defined such detection strategies for capturing around ten important flaws of object-oriented design found in the literature.
Abstract: In order to support the maintenance of an object-oriented software system, the quality of its design must be evaluated using adequate quantification means. In spite of the current extensive use of metrics, if used in isolation metrics are oftentimes too fine grained to quantify comprehensively an investigated design aspect (e.g., distribution of system's intelligence among classes). To help developers and maintainers detect and localize design problems in a system, we propose a novel mechanism - called detection strategy - for formulating metrics-based rules that capture deviations from good design principles and heuristics. Using detection strategies an engineer can directly localize classes or methods affected by a particular design flaw (e.g., God Class), rather than having to infer the real design problem from a large set of abnormal metric values. We have defined such detection strategies for capturing around ten important flaws of object-oriented design found in the literature and validated the approach experimentally on multiple large-scale case-studies.
591 citations