On applying machine learning techniques for design pattern detection

Zanoni, Marco; Fontana, Francesca Arcelli; Stella, Fabio

doi:10.1016/j.jss.2015.01.037

Cited by 89 publications

(56 citation statements)

References 43 publications

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“…This does not hold for Accuracy, Precision and Recall as they do not consider all types of correctly or incorrectly predicted instances (true/false positive, true/false negatives). Figure 2 shows a typical multistage DPD pipeline similar to many DP detectors [12], [13], [14], [15], [16], [17]. Rectangles are artifacts while ellipses are processes.…”

Section: B Machine Learningmentioning

confidence: 99%

Feature Maps: A Comprehensible Software Representation for Design Pattern Detection

Thaller

Linsbauer

Egyed

2019

2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Design patterns are elegant and well-tested solutions to recurrent software development problems. They are the result of software developers dealing with problems that frequently occur, solving them in the same or a slightly adapted way. A pattern's semantics provide the intent, motivation, and applicability, describing what it does, why it is needed, and where it is useful. Consequently, design patterns encode a well of information. Developers weave this information into their systems whenever they use design patterns to solve problems. This work presents Feature Maps, a flexible human-and machine-comprehensible software representation based on micro-structures. Our algorithm, the Feature-Role Normalization, presses the high-dimensional, inhomogeneous vector space of micro-structures into a feature map. We apply these concepts to the problem of detecting instances of design patterns in source code. We evaluate our methodology on four design patterns, a wide range of balanced and imbalanced labeled training data, and compare classical machine learning (Random Forests) with modern deep learning approaches (Convolutional Neural Networks). Feature maps yield robust classifiers even under challenging settings of strongly imbalanced data distributions without sacrificing human comprehensibility. Results suggest that feature maps are an excellent addition in the software analysis toolbox that can reveal useful information hidden in the source code.

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Section: B Machine Learningmentioning

confidence: 99%

Feature Maps: A Comprehensible Software Representation for Design Pattern Detection

Thaller

Linsbauer

Egyed

2019

2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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“…Fontana et al [22], presented a tool, MARPLE as an eclipse plugin for design patterns extraction and software architecture reconstruction. This tool is designed to be language independent, however, currently it available for Java.…”

Section: Related Workmentioning

confidence: 99%

Detection of J2EE Patterns based on Customizable Features

Mushtaq¹,

Rasool²,

Shahzad³

2017

ijacsa

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Abstract-Design patterns support extraction of design information for better program understanding, reusability and reengineering. With the advent of contemporary applications, the extraction of design information has become quite complex and challenging. These applications are multilingual in nature i.e. their design information is spread across various language components that are interlinked with each other. At present, no approach is available that is capable to extract design information of multilingual applications by using design patterns. This paper lays foundation for the analysis of multilingual source code for the detection of J2EE Patterns.

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“…Micro patterns are traceable, in the sense that they can be automatically recognized. In opposition, design patterns are not traceable [64] and many attempts have been made to formalize [65][66][67][68][69] and to automatically detect them [70][71][72][73][74][75].…”

Section: Other Kinds Of Patternsmentioning

confidence: 99%