The Experts below are selected from a list of 32151 Experts worldwide ranked by ideXlab platform
José A. Taboada - One of the best experts on this subject based on the ideXlab platform.
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Software Design Smell Detection: a systematic mapping study
Software Quality Journal, 2018Co-Authors: Khalid Alkharabsheh, Yania Crespo, Esperanza Manso, José A. TaboadaAbstract:Design Smells are indicators of situations that negatively affect software quality attributes such as understandability, testability, extensibility, reusability, and maintainability in general. Improving maintainability is one of the cornerstones of making software evolution easier. Hence, Design Smell Detection is important in helping developers when making decisions that can improve software evolution processes. After a long period of research, it is important to organize the knowledge produced so far and to identify current challenges and future trends. In this paper, we analyze 18 years of research into Design Smell Detection. There is a wide variety of terms that have been used in the literature to describe concepts which are similar to what we have defined as “Design Smells,” such as Design Defect, Design flaw, anomaly, pitfall, antipattern, and disharmony. The aim of this paper is to analyze all these terms and include them in the study. We have used the standard systematic literature review method based on a comprehensive set of 395 articles published in different proceedings, journals, and book chapters. We present the results in different dimensions of Design Smell Detection, such as the type or scope of smell, detection approaches, tools, applied techniques, validation evidence, type of artifact in which the smell is detected, resources used in evaluation, supported languages, and relation between detected smells and software quality attributes according to a quality model. The main contributions of this paper are, on the one hand, the application of domain modeling techniques to obtain a conceptual model that allows the organization of the knowledge on Design Smell Detection and a collaborative web application built on that knowledge and, on the other, finding how tendencies have moved across different kinds of smell detection, as well as different approaches and techniques. Key findings for future trends include the fact that all automatic detection tools described in the literature identify Design Smells as a binary decision (having the smell or not), which is an opportunity to evolve to fuzzy and prioritized decisions. We also find that there is a lack of human experts and benchmark validation processes, as well as demonstrating that Design Smell Detection positively influences quality attributes.
Shaan Huaping - One of the best experts on this subject based on the ideXlab platform.
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Analysis and Countermeasure on Typical External Insulation Failure of Main Equipments of ±500 kV Converter Stations
Power system technology, 2012Co-Authors: Shaan HuapingAbstract:±500 kV transmission lines have become an important way to connect the large area power grids in China.The basic information of ±500 kV point-to-point transmission lines in China was summarized;external insulation configurations for main equipments of DC fields in converter stations were compared;primary causes leading to external insulation failures occurred in main equipments of DC fields,namely insufficient configuration of external insulation,weakening or losing surface hydrophobicity of composite external insulation and Design Defect of main equipments,were pointed out;and pertinent countermeasures were given respectively.The types of discharge occurred on main equipments of DC fields in converter stations were classified and it was pointed out that for main equipments,on which the discharge in the second or the third level occurred,the RTV should be sprayed onto them or the booster shed should be added to them after the equipments were out of service.
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analysis and countermeasure on typical external insulation failure of main equipments of 500 kv converter stations
Power system technology, 2012Co-Authors: Shaan HuapingAbstract:±500 kV transmission lines have become an important way to connect the large area power grids in China.The basic information of ±500 kV point-to-point transmission lines in China was summarized;external insulation configurations for main equipments of DC fields in converter stations were compared;primary causes leading to external insulation failures occurred in main equipments of DC fields,namely insufficient configuration of external insulation,weakening or losing surface hydrophobicity of composite external insulation and Design Defect of main equipments,were pointed out;and pertinent countermeasures were given respectively.The types of discharge occurred on main equipments of DC fields in converter stations were classified and it was pointed out that for main equipments,on which the discharge in the second or the third level occurred,the RTV should be sprayed onto them or the booster shed should be added to them after the equipments were out of service.
Marouane Kessentini - One of the best experts on this subject based on the ideXlab platform.
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On the use of Design Defect examples to detect model refactoring opportunities
Software Quality Journal, 2016Co-Authors: Adnane Ghannem, Ghizlane El Boussaidi, Marouane KessentiniAbstract:Design Defects are symptoms of Design decay, which can lead to several maintenance problems. To detect these Defects, most of existing research is based on the definition of rules that represent a combination of software metrics. These rules are sometimes not enough to detect Design Defects since it is difficult to find the best threshold values; the rules do not take into consideration the programming context, and it is challenging to find the best combination of metrics. As an alternative, we propose in this paper to identify Design Defects using a genetic algorithm based on the similarity/distance between the system under study and a set of Defect examples without the need to define detection rules. We tested our approach on four open-source systems to identify three potential Design Defects. The results of our experiments confirm the effectiveness of the proposed approach.
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multi criteria code refactoring using search based software engineering an industrial case study
ACM Transactions on Software Engineering and Methodology, 2016Co-Authors: Ali Ouni, Marouane Kessentini, Houari Sahraoui, Katsuro InoueAbstract:One of the most widely used techniques to improve the quality of existing software systems is refactoring—the process of improving the Design of existing code by changing its internal structure without altering its external behavior. While it is important to suggest refactorings that improve the quality and structure of the system, many other criteria are also important to consider, such as reducing the number of code changes, preserving the semantics of the software Design and not only its behavior, and maintaining consistency with the previously applied refactorings. In this article, we propose a multi-objective search-based approach for automating the recommendation of refactorings. The process aims at finding the optimal sequence of refactorings that (i) improves the quality by minimizing the number of Design Defects, (ii) minimizes code changes required to fix those Defects, (iii) preserves Design semantics, and (iv) maximizes the consistency with the previously code changes. We evaluated the efficiency of our approach using a benchmark of six open-source systems, 11 different types of refactorings (move method, move field, pull up method, pull up field, push down method, push down field, inline class, move class, extract class, extract method, and extract interface) and six commonly occurring Design Defect types (blob, spaghetti code, functional decomposition, data class, shotgun surgery, and feature envy) through an empirical study conducted with experts. In addition, we performed an industrial validation of our technique, with 10 software engineers, on a large project provided by our industrial partner. We found that the proposed refactorings succeed in preserving the Design coherence of the code, with an acceptable level of code change score while reusing knowledge from recorded refactorings applied in the past to similar contexts.
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CASCON - Detecting model refactoring opportunities using heuristic search
2011Co-Authors: Adnane Ghannem, Marouane Kessentini, Ghizlane El BoussaidiAbstract:Model-driven engineering (MDE) is an approach to software development where the primary focus is on models. To improve their quality, models continually evolve due, for example, to the detection of "bad Design practices", called Design Defects. Presence of these Defects in a model suggests refactoring opportunities. Most of the research work that tackle the problem of detecting and correcting Defects, concentrate on source code. However, detecting Defects at the model level and during the Design process can be of great value to Designers in particular within an MDE process. In this paper, we propose an automated approach to detect model refactoring opportunities related to various types of Design Defects. Using Genetic Programming, our approach allows automatic generation of rules to detect Defects, thus relieving the Designer from a fastidious manual rule definition task. We evaluate our approach by finding three potential Design Defect types in two large class diagrams. For all these models, we succeed in detecting the majority of expected Defects.
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Design Defect detection rules generation a music metaphor
Conference on Software Maintenance and Reengineering, 2011Co-Authors: Marouane Kessentini, Houari Sahraoui, Mounir Boukadoum, Manuel WimmerAbstract:We propose an automated approach for Design Defect detection. It exploits an algorithm that automatically finds rules for the detection of possible Design Defects, thus relieving the Designer from doing so manually. Our algorithm derives rules in the form of metric/threshold combinations, from known instances of Design Defects (Defect examples). Due to the large number of possible combinations, we use a music-inspired heuristic that finds the best harmony when combining metrics. We evaluated our approach on finding potential Defects in three open-source systems (Xerces-J, Quick UML and Gantt). For all of them, we found more than 80% of known Defects, a better result when compared to a state-of-the-art approach, where the detection rules are manually specified.
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CSMR - Design Defect Detection Rules Generation: A Music Metaphor
2011 15th European Conference on Software Maintenance and Reengineering, 2011Co-Authors: Marouane Kessentini, Houari Sahraoui, Mounir Boukadoum, Manuel WimmerAbstract:We propose an automated approach for Design Defect detection. It exploits an algorithm that automatically finds rules for the detection of possible Design Defects, thus relieving the Designer from doing so manually. Our algorithm derives rules in the form of metric/threshold combinations, from known instances of Design Defects (Defect examples). Due to the large number of possible combinations, we use a music-inspired heuristic that finds the best harmony when combining metrics. We evaluated our approach on finding potential Defects in three open-source systems (Xerces-J, Quick UML and Gantt). For all of them, we found more than 80% of known Defects, a better result when compared to a state-of-the-art approach, where the detection rules are manually specified.
Khalid Alkharabsheh - One of the best experts on this subject based on the ideXlab platform.
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Software Design Smell Detection: a systematic mapping study
Software Quality Journal, 2018Co-Authors: Khalid Alkharabsheh, Yania Crespo, Esperanza Manso, José A. TaboadaAbstract:Design Smells are indicators of situations that negatively affect software quality attributes such as understandability, testability, extensibility, reusability, and maintainability in general. Improving maintainability is one of the cornerstones of making software evolution easier. Hence, Design Smell Detection is important in helping developers when making decisions that can improve software evolution processes. After a long period of research, it is important to organize the knowledge produced so far and to identify current challenges and future trends. In this paper, we analyze 18 years of research into Design Smell Detection. There is a wide variety of terms that have been used in the literature to describe concepts which are similar to what we have defined as “Design Smells,” such as Design Defect, Design flaw, anomaly, pitfall, antipattern, and disharmony. The aim of this paper is to analyze all these terms and include them in the study. We have used the standard systematic literature review method based on a comprehensive set of 395 articles published in different proceedings, journals, and book chapters. We present the results in different dimensions of Design Smell Detection, such as the type or scope of smell, detection approaches, tools, applied techniques, validation evidence, type of artifact in which the smell is detected, resources used in evaluation, supported languages, and relation between detected smells and software quality attributes according to a quality model. The main contributions of this paper are, on the one hand, the application of domain modeling techniques to obtain a conceptual model that allows the organization of the knowledge on Design Smell Detection and a collaborative web application built on that knowledge and, on the other, finding how tendencies have moved across different kinds of smell detection, as well as different approaches and techniques. Key findings for future trends include the fact that all automatic detection tools described in the literature identify Design Smells as a binary decision (having the smell or not), which is an opportunity to evolve to fuzzy and prioritized decisions. We also find that there is a lack of human experts and benchmark validation processes, as well as demonstrating that Design Smell Detection positively influences quality attributes.
Yania Crespo - One of the best experts on this subject based on the ideXlab platform.
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Software Design Smell Detection: a systematic mapping study
Software Quality Journal, 2018Co-Authors: Khalid Alkharabsheh, Yania Crespo, Esperanza Manso, José A. TaboadaAbstract:Design Smells are indicators of situations that negatively affect software quality attributes such as understandability, testability, extensibility, reusability, and maintainability in general. Improving maintainability is one of the cornerstones of making software evolution easier. Hence, Design Smell Detection is important in helping developers when making decisions that can improve software evolution processes. After a long period of research, it is important to organize the knowledge produced so far and to identify current challenges and future trends. In this paper, we analyze 18 years of research into Design Smell Detection. There is a wide variety of terms that have been used in the literature to describe concepts which are similar to what we have defined as “Design Smells,” such as Design Defect, Design flaw, anomaly, pitfall, antipattern, and disharmony. The aim of this paper is to analyze all these terms and include them in the study. We have used the standard systematic literature review method based on a comprehensive set of 395 articles published in different proceedings, journals, and book chapters. We present the results in different dimensions of Design Smell Detection, such as the type or scope of smell, detection approaches, tools, applied techniques, validation evidence, type of artifact in which the smell is detected, resources used in evaluation, supported languages, and relation between detected smells and software quality attributes according to a quality model. The main contributions of this paper are, on the one hand, the application of domain modeling techniques to obtain a conceptual model that allows the organization of the knowledge on Design Smell Detection and a collaborative web application built on that knowledge and, on the other, finding how tendencies have moved across different kinds of smell detection, as well as different approaches and techniques. Key findings for future trends include the fact that all automatic detection tools described in the literature identify Design Smells as a binary decision (having the smell or not), which is an opportunity to evolve to fuzzy and prioritized decisions. We also find that there is a lack of human experts and benchmark validation processes, as well as demonstrating that Design Smell Detection positively influences quality attributes.