Francesca Arcelli Fontana

Bio: Francesca Arcelli Fontana is an academic researcher from University of Milano-Bicocca. The author has contributed to research in topics: Code smell & Software quality. The author has an hindex of 28, co-authored 98 publications receiving 2258 citations. Previous affiliations of Francesca Arcelli Fontana include University of Salerno & University of Milan.

Comparing and experimenting machine learning techniques for code smell detection

Abstract: Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.

Automatic detection of bad smells in code: An experimental assessment

Abstract: Code smells are structural characteristics of software that may indicate a code or design problem that makes software hard to evolve and maintain, and may trigger refactoring of code. Recent research is active in defining automatic detection tools to help humans in finding smells when code size becomes unmanageable for manual review. Since the definitions of code smells are informal and subjective, assessing how effective code smell detection tools are is both important and hard to achieve. This paper reviews the current panorama of the tools for automatic code smell detection. It defines research questions about the consistency of their responses, their ability to expose the regions of code most affected by structural decay, and the relevance of their responses with respect to future software evolution. It gives answers to them by analyzing the output of four representative code smell detectors applied to six different versions of GanttProject, an open source system written in Java. The results of these experiments cast light on what current code smell detection tools are able to do and what the relevant areas for further improvement are.

Code Smell Detection: Towards a Machine Learning-Based Approach

Abstract: Several code smells detection tools have been developed providing different results, because smells can be subjectively interpreted and hence detected in different ways. Usually the detection techniques are based on the computation of different kinds of metrics, and other aspects related to the domain of the system under analysis, its size and other design features are not taken into account. In this paper we propose an approach we are studying based on machine learning techniques. We outline some common problems faced for smells detection and we describe the different steps of our approach and the algorithms we use for the classification.

Arcan: A Tool for Architectural Smells Detection

Abstract: Code smells are sub-optimal coding circumstances such as blob classes or spaghetti code - they have received much attention and tooling in recent software engineering research. Higher-up in the abstraction level, architectural smells are problems or sub-optimal architectural patterns or other design-level characteristics. These have received significantly less attention even though they are usually considered more critical than code smells, and harder to detect, remove, and refactor. This paper describes an open-source tool called Arcan developed for the detection of architectural smells through an evaluation of several different architecture dependency issues. The detection techniques inside Arcan exploit graph database technology, allowing for high scalability in smells detection and better management of large amounts of dependencies of multiple kinds. In the scope of this paper, we focus on the evaluation of Arcan results carried out with real-life software developers to check if the architectural smells detected by Arcan are really perceived as problems and to get an overall usefulness evaluation of the tool.

Code smell severity classification using machine learning techniques

Abstract: Several code smells detection tools have been developed providing different results, because smells can be subjectively interpreted and hence detected in different ways. Machine learning techniques have been used for different topics in software engineering, e.g., design pattern detection, code smell detection, bug prediction, recommending systems. In this paper, we focus our attention on the classification of code smell severity through the use of machine learning techniques in different experiments. The severity of code smells is an important factor to take into consideration when reporting code smell detection results, since it allows the prioritization of refactoring efforts. In fact, code smells with high severity can be particularly large and complex, and create larger issues to the maintainability of software a system. In our experiments, we apply several machine learning models, spanning from multinomial classification to regression, plus a method to apply binary classifiers for ordinal classification. In fact, we model code smell severity as an ordinal variable. We take the baseline models from previous work, where we applied binary classification models for code smell detection with good results. We report and compare the performance of the models according to their accuracy and four different performance measures used for the evaluation of ordinal classification techniques. From our results, while the accuracy of the classification of severity is not high as in the binary classification of absence or presence of code smells, the ranking correlation of the actual and predicted severity for the best models reaches 0.880.96, measured through Spearmans .

An overview of AspectJ

Abstract: Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

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