Leon Moonen

Java quality assurance by detecting code smells

Software inspection is a known technique for improving software quality. It involves carefully examining the code, the design, and the documentation of software and checking these for aspects that are known to be potentially problematic based on past experience. Code smells are a metaphor to describe patterns that are generally associated with bad design and bad programming practices. Originally, code smells are used to find the places in software that could benefit from refactoring. In this paper we investigate how the quality of code can be automatically assessed by checking for the presence of code smells and how this approach can contribute to automatic code inspection. We present an approach for the automatic detection and visualization of code smells and discuss how this approach can be used in the design of a software inspection tool. We illustrate the feasibility of our approach with the development of jCOSMO, a prototype code smell browser that detects and visualizes code smells in JAVA source code. Finally, we show how this tool was applied in a case study.

Identifying aspects using fan-in analysis

The issues of code scattering and tangling, thus of achieving a better modularity for a systems concerns, are addressed by the paradigm of aspect orientation. Aspect mining is a reverse engineering process that aims at finding crosscutting concerns in existing systems. This work describes a technique based on determining methods that are called from many different places (and hence have a high fan-in) to identify candidate aspects in a number of open-source Java systems. The most interesting aspects identified are discussed in detail, which includes several concerns not previously discussed in the aspect-oriented literature. The results show that a significant number of aspects can be recognized using fan-in analysis, and that the technique is suitable for a high degree of automation.

Symphony: view-driven software architecture reconstruction

Authentic descriptions of a software architecture are required as a reliable foundation for any but trivial changes to a system. Far too often, architecture descriptions of existing systems are out of sync with the implementation. If they are, they must be reconstructed. There are many existing techniques for reconstructing individual architecture views, but no information about how to select views for reconstruction, or about process aspects of architecture reconstruction in general. In this paper we describe view-driven process for reconstructing software architecture that fills this gap. To describe Symphony, we present and compare different case studies, thus serving a secondary goal of sharing real-life reconstruction experience. The Symphony process incorporates the state of the practice, where reconstruction is problem-driven and uses a rich set of architecture views. Symphony provides a common framework for reporting reconstruction experiences and for comparing reconstruction approaches. Finally, it is a vehicle for exposing and demarcating research problems in software architecture reconstruction.

Identifying Crosscutting Concerns Using Fan-In Analysis

Aspect mining is a reverse engineering process that aims at finding crosscutting concerns in existing systems. This article proposes an aspect mining approach based on determining methods that are called from many different places, and hence have a high fan-in, which can be seen as a symptom of crosscutting functionality. The approach is semiautomatic, and consists of three steps: metric calculation, method filtering, and call site analysis. Carrying out these steps is an interactive process supported by an Eclipse plug-in called FINT. Fan-in analysis has been applied to three open source Java systems, totaling around 200,000 lines of code. The most interesting concerns identified are discussed in detail, which includes several concerns not previously discussed in the aspect-oriented literature. The results show that a significant number of crosscutting concerns can be recognized using fan-in analysis, and each of the three steps can be supported by tools.

Understanding Execution Traces Using Massive Sequence and Circular Bundle Views

The use of dynamic information to aid in software understanding is a common practice nowadays. One of the many approaches concerns the comprehension of execution traces. A major issue in this context is scalability: due to the vast amounts of information, it is a very difficult task to successfully find your way through such traces without getting lost. In this paper, we propose the use of a novel trace visualization method based on a massive sequence and circular bundle view, constructed with scalability in mind. By means of three usage scenarios that were conducted on three different software systems, we show how our approach, implemented in a tool called EXTRAVIS, is applicable to the areas of trace exploration, feature location, and feature comprehension.

Exploring the impact of inter-smell relations on software maintainability: an empirical study

Code smells are indicators of issues with source code quality that may hinder evolution. While previous studies mainly focused on the effects of individual code smells on maintainability, we conjecture that not only the individual code smells but also the interactions between code smells affect maintenance. We empirically investigate the interactions amongst 12 code smells and analyze how those interactions relate to maintenance problems. Professional developers were hired for a period of four weeks to implement change requests on four medium-sized Java systems with known smells. On a daily basis, we recorded what specific problems they faced and which artifacts were associated with them. Code smells were automatically detected in the pre-maintenance versions of the systems and analyzed using Principal Component Analysis (PCA) to identify patterns of co-located code smells. Analysis of these factors with the observed maintenance problems revealed how smells that were co-located in the same artifact interacted with each other, and affected maintainability. Moreover, we found that code smell interactions occurred across coupled artifacts, with comparable negative effects as same-artifact co-location. We argue that future studies into the effects of code smells on maintainability should integrate dependency analysis in their process so that they can obtain a more complete understanding by including such coupled interactions.

Execution trace analysis through massive sequence and circular bundle views

Preprint of article published in: Journal of Systems and Software (Elsevier), 81 (12), 2008; doi:10.1016/j.jss.2008.02.068 An important part of many software maintenance tasks is to gain a sufficient level of understanding of the system at hand. The use of dynamic information to aid in this software understanding process is a common practice nowadays. A major issue in this context is scalability: due to the vast amounts of information, it is a very difficult task to successfully navigate through the dynamic data contained in execution traces without getting lost. In this paper, we propose the use of two novel trace visualization techniques based on the massive sequence and circular bundle view, which both reflect a strong emphasis on scalability. These techniques have been implemented in a tool called Extravis. By means of distinct usage scenarios that were conducted on three different software systems, we show how our approach is applicable in three typical program comprehension tasks: trace exploration, feature location, and top-down analysis with domain knowledge.

A qualitative comparison of three aspect mining techniques

The fact that crosscutting concerns (aspects) cannot be well modularized in object oriented software is an impediment to program comprehension: the implementation of a concern is typically scattered over many locations and tangled with the implementation of other concerns, resulting in a system that is hard to explore and understand. Aspect mining aims to identify crosscutting concerns in a system, thereby improving the systems comprehensibility and enabling migration of existing (object-oriented) programs to aspect-oriented ones. In this paper, we compare three aspect mining techniques that were developed independently by different research teams: fan-in analysis, identifier analysis and dynamic analysis. We apply each technique to the same case (JHotDraw) and mutually compare the individual results of each technique based on the discovered aspects and on the level of detail and quality of those aspects. Strengths, weaknesses and underlying assumptions of each technique are discussed, as well as their complementarity. We conclude with a discussion of possible ways to combine the techniques in order to achieve a better overall aspect-mining technique.

Applying and combining three different aspect Mining Techniques

Understanding a software system at source-code level requires understanding the different concerns that it addresses, which in turn requires a way to identify these concerns in the source code. Whereas some concerns are explicitly represented by program entities (like classes, methods and variables) and thus are easy to identify, crosscutting concerns are not captured by a single program entity but are scattered over many program entities and are tangled with the other concerns. Because of their crosscutting nature, such crosscutting concerns are difficult to identify, and reduce the understandability of the system as a whole.In this paper, we report on a combined experiment in which we try to identify crosscutting concerns in the JHotDraw framework automatically. We first apply three independently developed aspect mining techniques to JHotDraw and evaluate and compare their results. Based on this analysis, we present three interesting combinations of these three techniques, and show how these combinations provide a more complete coverage of the detected concerns as compared to the original techniques individually. Our results are a first step towards improving the understandability of a system that contains crosscutting concerns, and can be used as a basis for refactoring the identified crosscutting concerns into aspects.

A classification of crosscutting concerns

Refactoring software to apply aspect oriented solutions requires a clear understanding of what are the potential crosscutting concerns and which aspect solutions to replace them with. This process can benefit from the recognition of recurring generic concerns and their reusable aspect solutions. In this paper, we propose a classification of crosscutting concerns in sorts based on the analysis of various refactoring efforts. We discuss how sorts help concern understanding and refactoring, how they support the identification of crosscutting concerns, and how they can contribute to the evolution of aspect languages.