Mining Association Rules from Code (MARC) to support legacy software management

Tjortjis, Christos

doi:10.1007/s11219-019-09480-3

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Functions were considered as entities in their study, and the following features were used: function call (functions called by an entity), global variables (global variables referred to by an entity), and user-defined types (user-defined types accessed by an entity). Tjortjis [29] proposed a method for mining association rules from code in order to capture program structure and achieve a better understanding of the system. To classify code chunks as entities, they used the following characteristics: Code blocks, variables, and data types are all entitled.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

Khan

Naseem

Anwar

et al. 2022

Journal of Mathematics

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Complex software systems that support organizations are updated regularly, which can erode system architectures. Moreover, documentation is rarely synchronized with the changes to the software system. This creates a slew of issues for future software maintenance. To this goal, information extraction tools use exact approaches to extract entities and their corresponding relationships from source code. Such exact approaches extract all features, including those that are less prominent and may not be significant for modularization. In order to resolve the issue, this work proposes an enhanced approximate information extraction approach, namely, fact extractor system for Java applications (FESJA) that aims to automate software modularization using a fact extraction system. The proposed FESJA technique extracts all the entities along with their corresponding more dominant formal and informal relationships from a Java source code. Results demonstrate the improved performance of FESJA, by extracting 74 (classes), 43 (interfaces), and 31 (enumeration), in comparison with eminent information extraction techniques.

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Section: Related Workmentioning

confidence: 99%

[Retracted] A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

Khan

Naseem

Anwar

et al. 2022

Journal of Mathematics

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“…where (o, r) is the interactive learning information coordinate, d is the optimization coefficient, and i represents the optimization times. Using the method of group sample regression analysis, the association rules of English-aided instruction systems are mined in a web environment [17]. rough statistical analysis and fusion decision, the fitness function of English-aided instruction system information is obtained:…”

Section: Mining the English Course Learning Resources In Thementioning

confidence: 99%

The Design and Application of College English-Aided Teaching System Based on Web

Chen

2022

Mobile Information Systems

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With the rapid advancement of information technology, the Internet and the web have become the central technology tools for information sharing in education and teaching. Modern education combines teaching with information technology and gives full play to the role of information technology in education and teaching. To improve the resource sharing and scheduling level a web-based college English-assisted teaching system is presented in this study. Using the JavaScript object notation syntax (JSON) as the data communication format between the intelligent terminal and the server, the system is implemented using a web server and mobile Android terminal, and a web-based data integration module is designed. Statistical data mining and fuzzy feature detection are employed to implement the decision-making scheduling and self-adaptive optimization of English teaching resources in a web aided English teaching system. The fusion particle swarm optimization algorithm is used to integrate English teaching resources and implement adaptive scheduling. To verify the application performance of the designed system, a simulation experiment is designed. The experimental results show that the proposed English-assisted instruction system has good convergence and high accuracy of learning information, optimal allocation, and scheduling.

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“…Tjortjis [25] has presented a method for Mining Association Rules from Code (MARC) to capture program structure, system knowledge, and assisting software management. Code parsing, rule mining, and rule grouping have been included in the methodology.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection

Thessalonica

Nehemiah

Sreejith

et al. 2023

Scientific Programming

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Software maintenance is an imperative step in software development. Code smells can arise as a result of poor design as well as frequent code changes due to changing needs. Early detection of code smells during software development can help with software maintenance. This work focuses on identifying code smells on Java software using nanopatterns. Nanopatterns are method-level code structures that reflect the presence of code smells. Nanopatterns are extracted using a command-line interface based on the ASM bytecode analysis. Class labels are extracted using three tools, namely inFusion, JDeodorant, and iPlasma. Rules are extracted from nanopatterns using the Apriori algorithm and mapped with the extracted class labels. Best rules are selected using the Border Collie Optimization (BCO) algorithm with the accuracy of the k-NN classifier as the fitness function. The selected rules are stored in the rule base to detect code smells. The objective is to detect a maximum number of code smells with a minimum number of rules. Experiments are carried out on Java software, namely jEdit, Nutch, Lucene, and Rhino. The proposed work detects code smells, namely data class, blob, spaghetti code, functional decomposition, and feature envy, with 98.78% accuracy for jEdit, 97.45% for Nutch, 95.58% for Lucene, and 96.34% for Rhino. The performance of the proposed work is competitive with other well-known methods of detecting code smells.

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Mining Association Rules from Code (MARC) to support legacy software management

Cited by 6 publications

References 28 publications

[Retracted] A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

[Retracted] A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

The Design and Application of College English-Aided Teaching System Based on Web

Intelligent Mining of Association Rules Based on Nanopatterns for Code Smells Detection

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