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[서평]「Applying UML and Patterns - An Introduction to Object-Oriented Analysis and Design」

https://zenodo.org/record/1997449/files/SS18.pdf

A survey on software smells

The presence of code and design smells can have a severe impact on the quality of a program. Consequently, their detection and correction have drawn the attention of both researchers and practitioners who have proposed various approaches to detect code and design smells in programs. However, none of these approaches handle the inherent uncertainty of the detection process. We propose a Bayesian approach to manage this uncertainty. First, we present a systematic process to convert existing state-of-the-art detection rules into a probabilistic model. We illustrate this process by generating a model to detect occurrences of the Blob antipattern. Second, we present results of the validation of the model: we built this model on two open-source programs, GanttProject v1.10.2 and Xerces v2.7.0, and measured its accuracy. Third, we compare our model with another approach to show that it returns the same candidate classes while ordering them to minimise the quality analysts' effort. Finally, we show that when past detection results are available, our model can be calibrated using machine learning techniques to offer an improved, context-specific detection.

A GQM-based Method and a Bayesian Approach for the Detection of Code and Design Smells

Managing traceability data is an important aspect of the software development process. In this paper we investigate to what extent latent semantic indexing (LSI), an information retrieval technique, can help recovering the information needed for automatically reconstructing traceability during the development process. We experimented with two different link selection strategies and applied LSI in multiple case studies varying in size and context. We discuss the results of a small lab study, a larger case study and a large industrial case study.

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Can LSI help reconstructing requirements traceability in design and test

Anti-patterns are poor solutions of recurring design problems, which decrease software quality. Numerous anti-patterns have been outlined in the literature as violations of various quality rules. Most of these anti-patterns have been defined in terms of code quality metrics. However, identifying anti-patterns at the design level would improve considerably the code quality and substantially reduce the cost of correcting their effects during the coding and maintenance phases. Within this context, we propose an approach that identifies anti-patterns in UML designs through the use of existing and newly defined quality metrics. Operating at the design level, our approach examines structural and behavioral information through the class and sequence diagrams. It is illustrated through five, well-known anti-patterns: Blob, Lava Flow, Functional Decomposition, Poltergeists, and Swiss Army Knife.

A Metric-Based Approach for Anti-pattern Detection in UML Designs

The detection of code smells is a fundamental prerequisite f or guiding the subsequent steps in the refactoring process. The more the detection results are accurate, the mo re the performance of the refactoring on the software is improved. Given its influential role in the softw are maintenance, this challenging research topic has so far attracted an increasing interest. However, the la ck of consensus about the definition of code smells in the literature has led to a considerable diversity of the exi sting results. To reduce the confusion associated with this lack of consensus, there is a real need to achieve a deep a nd consistent representation of the code smells. Recently, the advance of deep learning has demonstrated an u nde iable contribution in many research fields including the pattern recognition issues. In this paper, we propose a hybrid detection approach based on deep Auto-encoder and Artificial Neural Network algorithms. Fou r code smells (God Class, Data Class, Feature Envy and Long Method) are the focus of our experiment on four a dopted datasets that are extracted from 74 open source systems. The values of recall and precision me asurements have demonstrated high accuracy results.

A Hybrid Approach To Detect Code Smells using Deep Learning.

Design patterns represent high-level recurring abstractions that reflect the know-how of experts. Their detection is a key task in the context of software engineering; it is very useful in program comprehension, design recovery and also for re-documenting object-oriented systems. Despite their usefulness, current design pattern detection approaches have a high time complexity that hinders their application. This complexity is further aggravated with the absence of guiding principles in determining which pattern to look for first among the various patterns. To overcome this practical limit, we propose to optimize any pattern detection approach by foregoing it by a filtering phase that detects possible existence of patterns and that orders the candidate patterns in terms of their degree of resemblance to the analyzed design fragments. The herein proposed filtering approach exploits semantic and structural design metrics to look for the semantic and structural symptoms of design pattern instances. Its performance is experimentally demonstrated through our design pattern detection method MAPeD (Multi-phase Approach for Pattern Discovery) applied on the open source system JUnit.

Using metric-based filtering to improve design pattern detection approaches

Mining Feature Models from Functional Requirements

Design patterns describe solutions for well-known design problems. They were proposed to improve software quality and accelerate software development. However, there are several limitations slowing their large-scale expansion such as the complexity of their comprehension and the difficulty of choosing and instantiating the right pattern adapted to a certain application. In this context, we propose a tool for design pattern assistance that retrieves the adequate pattern from a repository of patterns. It identifies the correct design pattern and helps the designer in its instantiation.

Nadia Bouassida

Papers

A Metric-Based Approach for Anti-pattern Detection in UML Designs

A Hybrid Approach To Detect Code Smells using Deep Learning.

Using metric-based filtering to improve design pattern detection approaches

Mining Feature Models from Functional Requirements

A design pattern recommendation approach