Feature Maps: A Comprehensible Software Representation for Design Pattern Detection

Thaller, Hannes; Linsbauer, Lukas; Egyed, Alexander

doi:10.1109/saner.2019.8667978

Cited by 24 publications

(34 citation statements)

References 22 publications

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“…MLDA can recover patterns' instances based on a generated classlevel representation of an investigated system. Another work has proposed Feature Maps, a flexible machine-comprehensible software representation based on micro-structures to detect patterns (Thaller et al, 2019). This algorithm is the Feature-Role normalization and presses the high-dimensional, inhomogeneous vector space of micro-structures into a feature map.…”

Section: Related Workmentioning

confidence: 99%

A new method for detecting various variants of GoF design patterns using conceptual signatures

et al. 2021

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Software design patterns are well-known solutions for solving commonly occurring problems in software design. Detecting design patterns used in the code can help to understand the structure and behavior of the software, evaluate the quality of the software, and trace important design decisions. To develop and maintain a software system, we need sufficient knowledge of design decisions and software implementation processes. However, the acquisition of knowledge related to design patterns used in complex software systems is a challenging, time-consuming, and costly task. Therefore, using a suitable method to detect the design patterns used in the code reduces software development and maintenance costs. In this paper, we proposed a new method based on conceptual signatures to improve the accuracy of design pattern detection. So we used the conceptual signatures based on the purpose of patterns to detect the patterns' instances that conform to the standard structure of patterns, and cover more instances of patterns' variants and implementation versions of the patterns and improve the accuracy of pattern detection. The proposed method is a specific process in two main phases. In the first phase, the conceptual signature and detection formula for each pattern is determined manually. Then in the second phase, each pattern in the code is detected in a semi-automatic process using the conceptual signature and pattern detection formula. To implement the proposed method, we focused on GoF design patterns and their variants. We evaluated the accuracy of our proposed method on five open-source projects, namely, Junit v3.7, JHotDraw v5.1, QuickUML 2001, JRefactory v2.6.24, and MapperXML v1.9.7. Also, we performed our experiments on a set of source codes containing the instances of GoF design patterns' variants for a comprehensive and fair evaluation. The evaluation results indicate that the proposed method has improved the accuracy of design pattern detection in the code.

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Section: Related Workmentioning

confidence: 99%

A new method for detecting various variants of GoF design patterns using conceptual signatures

et al. 2021

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“…Code search [15,44,45,134,138], Code classification [30,74,154] code readability classification/prediction [105,117], Function type inferring [53,103] Code generation [35,115], Code Summarization [75,135], Code Decompilation [66,71] Code change generation [130], Data structure classification [108], Reverse execution [111] Design pattern detection [127], Technical debt detection [118], Story points prediction [21] Inconsistent method name refactoring [92], Stable patch detection [55] Defect Defect prediction [94,98,129,136,137,140], Vulnerability prediction [27,38,52,151] 8 24 Bug localization/detection [59,72,81,139,144,155] , Code repair [10,132,141] Code smell detection …”

Section: Codementioning

confidence: 99%

On the Replicability and Reproducibility of Deep Learning in Software Engineering

Liu,

Gao,

Xia

et al. 2020

Preprint

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Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge. Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) replicability -whether the reported experimental result can be approximately reproduced in high probability with the same DL model and the same data; and (2) reproducibility -whether one reported experimental findings can be reproduced by new experiments with the same experimental protocol and DL model, but different sampled real-world data. Unlike traditional machine learning (ML) models, DL studies commonly overlook these two factors and declare them as minor threats or leave them for future work. This is mainly due to high model complexity with many manually set parameters and the time-consuming optimization process. In this study, we conducted a literature review on 93 DL studies recently published in twenty SE journals or conferences. Our statistics show the urgency of investigating these two factors in SE, where only 10.8% of the studies discussed any research questions affecting replicability and/or reproducibility. More than 74.2% of the studies do not even share source code and data to support the replicability of their complex models. Moreover, we re-ran four representative DL models in SE. Experimental results show the importance of replicability and reproducibility, where the reported performance of a DL model could not be replicated for an unstable optimization process. Reproducibility could be substantially compromised if the model training is not convergent, or if performance is sensitive to the size of vocabulary and testing data. It is therefore urgent for the SE community to provide a long-lasting link to a replication package, enhance DL-based solution stability and convergence, and avoid performance sensitivity on different sampled data.CCS Concepts: • Software and its engineering → Software maintenance tools.

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“…Machine Learning (ML) builds a model that learns from the data in an automated fashion (Thaller et al, 2019). In ML the essential elements are data -structural such as text, unstructural or semi-structural such as source code, the model e.g., Support vector Machines (SVM) and Random Forest (RF) (Cortes and Vapnik, 1995;Ho, 1995), and the evaluation procedure e.g., Cross-Validation.…”

Section: Machine Learningmentioning

confidence: 99%

“…To do so, we select following two studies that are Fea-tureMaps & MARPLE-DPD proposed by Thaller et al (2019) and Zanoni et al (2015) respectively as a benchmark studies for comparing our approach. Theses studies as well as the details of the benchmark corpus are discussed in detail in Section 3.2…”

Section: Research Questionsmentioning

confidence: 99%

“…To ensure that sufficient instances are available for training and testing the machine learning algorithms, we select at least 100 instances for each of the 12 design patterns. Benchmark Corpus: In addition to the DPD coprus, we employ an existing dataset (Zanoni et al, 2015;Thaller et al, 2019), which contains 215 files from JHotDraw, JRefactory, JUnit, Apache Nutch, pmd, Eclipse JDT and Apache Ant. To avoid the class imbalance problem, we ensure that the benchmark corpus has at least 20 instances for each pattern, which resulted in seven patterns in the benchmark corpus.…”

Section: Data Collectionmentioning

confidence: 99%

See 1 more Smart Citation

Feature-Based Software Design Pattern Detection

Nazar¹,

Aleti²,

Zheng³

2020

Preprint

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Software design patterns are standard solutions to common problems in software design and architecture. Knowing that a particular module implements a design pattern is a shortcut to design comprehension. Manually detecting design patterns is a time consuming and challenging task; therefore, researchers have proposed automatic design patterns detection techniques to facilitate software developers. However, these techniques show low performance for certain design patterns. In this work, we introduce an approach that improves the performance over the state-of-the-art by using code features with machine learning classifiers to automatically train a design pattern detection. We create a semantic representation of source code from the code features and the call graph, and apply the Word2Vec algorithm on the semantic representation to construct the word-space geometric model of the Java source code. DPD then uses a Machine Learning approach trained using the word-space model and identifies software design patterns with 78% Precision and 76% Recall. Additionally, we have compared our results with two existing design pattern detection approaches namely FeatureMaps & MARPLE-DPD. Empirical results demonstrate that our approach outperforms the benchmark approaches by 30% and 10% respectively in terms of Precision. The runtime performance also supports its practical applicability.

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Feature Maps: A Comprehensible Software Representation for Design Pattern Detection

Cited by 24 publications

References 22 publications

A new method for detecting various variants of GoF design patterns using conceptual signatures

A new method for detecting various variants of GoF design patterns using conceptual signatures

On the Replicability and Reproducibility of Deep Learning in Software Engineering

Feature-Based Software Design Pattern Detection

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