On clustering based aspect mining

Cojocar, Grigoreta Sofia; Czibula, Gabriela

doi:10.1109/iccp.2008.4648364

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…Various static concern mining techniques have been proposed: Fan-in value [25], identifier analysis [36,39], clone detection [7,34], random walks [38], Latent Dirichlet Allocation [1], clustering [12,39] or even a mix of techniques [33]. The techniques based on inexact equality like clone detection techniques [7,34] and PAM [38] deal better with slight variations in concern instances than CBFA and HAM, but are not able to handle larger variations like near clones (S1).…”

Section: Related Workmentioning

confidence: 99%

Identifying crosscutting concerns using historical code changes

Adams

Jiang

Hassan

2010

Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1

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Detailed knowledge about implemented concerns in the source code is crucial for the cost-effective maintenance and successful evolution of large systems. Concern mining techniques can automatically suggest sets of related code fragments that likely contribute to the implementation of a concern. However, developers must then spend considerable time understanding and expanding these concern seeds to obtain the full concern implementation. We propose a new mining technique (COMMIT) that reduces this manual effort. COMMIT addresses three major shortcomings of current concern mining techniques: 1) their inability to merge seeds with small variations, 2) their tendency to ignore important facets of concerns, and 3) their lack of information about the relations between seeds. A comparative case study on two large open source C systems (Post-greSQL and NetBSD) shows that COMMIT recovers up to 87.5% more unique concerns than two leading concern mining techniques, and that the three techniques complement each other.

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Section: Related Workmentioning

confidence: 99%

Identifying crosscutting concerns using historical code changes

Adams

Jiang

Hassan

2010

Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1

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“…These techniques focus especially on crosscutting concerns, as modular concerns can be easily identified manually. Depending on the intended usage of a technique, it can be applied as frequently as once per release (for documentation) up until once per feature request or even bug report [28].…”

Section: Mining Of Crosscutting Concernsmentioning

confidence: 99%

“…In our work, we use the clustering-based technique which aims at identifying groups of methods or statements related to the crosscutting concerns guided by a distance measure [28][29][30]. Clustering is a division of data into groups of similar objects where each of these subsets (groups, clusters) consists of objects that are similar between themselves and dissimilar to objects of other groups.…”

Section: Mining Of Crosscutting Concernsmentioning

confidence: 99%

A combined approach for concern identification in KDM models

2015

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Background: Systems are considered legacy when their maintenance costs raise to unmanageable levels, but they still deliver valuable benefits for companies. One intrinsic problem of this kind of system is the presence of crosscutting concerns in their architecture, hindering its comprehension and evolution. Architecture-driven modernization (ADM) is the new generation of reengineering in which models are used as main artifacts during the whole process. Using ADM, it is possible to modernize legacy systems by remodularizing their concerns in a more modular shape. In this sense, the first step is the identification of source code elements that contribute to the implementation of those concerns, a process known as concern mining. Although there exist a number of concern mining approaches in the literature, none of them are devoted to ADM, leading individual groups to create their own ad hoc proprietary solutions. In this paper, we propose an approach called crosscutting-concern knowledge discovery meta-model (CCKDM) whose goal is to mine crosscutting concerns in ADM context. Our approach employs a combination of a concern library and a K-means clustering algorithm. Methods: We have conducted an experimental study composed of two analyses. The first one aimed to identify the most suitable levenshtein values to apply the clustering algorithm. The second one aimed to check the recall and precision of our approach when compared to oracles and also to two other existing mining techniques (XScan and Timna) found in literature. Results: The main result of this work is a combined mining approach for KDM that enables a concern-oriented modernization to be performed. As a secondary and more general result, this work shows that it is possible to adapt existing concern mining code-level approaches for being used in ADM processes and maintain the same level of precision and recall. Conclusions: By using the approach herein presented, it was possible to conclude the following: (i) it is possible to automate the identification of crosscutting concerns in KDM models and (ii) the results are similar or equal to other approaches.

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“…Applying data mining [1] techniques in order to extract meaningful knowledge from various data types is of great interest, being used for improving decision-making processes in various domains. Machine learning (ML) [2] offers a wide range of models and techniques for uncovering hidden patterns in data from numerous practical domains, such as bioinformatics (for protein dynamics analysis [3], [4]), mete-orology (for precipitation nowcasting and radar data analysis [5], [6]), software engineering (for software structure analysis [7] and restructuring [8], aspect mining [9]), medicine (for clinical decision support [10] and medical data analysis [11]), computer vision (for image analysis [12]), educational data mining (for academic data analysis [13], [14]), etc. Educational data mining (EDM) is a domain of research that applies data mining, ML, and statistics to data obtained from educational contexts.…”

Section: Introductionmentioning

confidence: 99%

IntelliDaM: A Machine Learning-Based Framework for Enhancing the Performance of Decision-Making Processes. A Case Study for Educational Data Mining

et al. 2022

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Nowadays, both predictive and descriptive modelling play a key role in decision-making processes in almost every branch of activity. In this article we are introducing IntelliDaM , a generic machine learning-based framework useful for improving the performance of data mining tasks and subsequently enhancing decision-making processes. Through its components designed for feature analysis, unsupervised and supervised learning-based data mining, IntelliDaM facilitates hidden knowledge discovery from data. Intensive research has been conducted in the field of educational data mining, as education institutions are interested in constantly adapting their educational programs to the needs of society by improving the quality of managerial decisions, course instructors' decision-making, or information gathering for course design. The present work conducts a longitudinal educational data mining study by applying IntelliDaM to real data collected at Babeş-Bolyai University, Romania, for a Computer Science course. The problem of mining educational data has been thoroughly examined using the proposed framework, with the goal of analysing students' performance. A very good performance has been achieved for the classification task (an F 1 score of around 92%), and the results also highlighted a statistically significant performance improvement by using a technique for selecting discriminative data features. The performed study confirmed that IntelliDaM could be a useful instrument in educational environments, particularly for improving decision-making processes, like designing courses, the setup of efficient examinations, avoiding plagiarism, or offering support regarding stress management.INDEX TERMS Data mining, Educational data mining, Machine learning, Students' performance analysis and prediction.

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On clustering based aspect mining

Cited by 9 publications

References 16 publications

Identifying crosscutting concerns using historical code changes

Identifying crosscutting concerns using historical code changes

A combined approach for concern identification in KDM models

IntelliDaM: A Machine Learning-Based Framework for Enhancing the Performance of Decision-Making Processes. A Case Study for Educational Data Mining

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