Yann-Gaël Guéhéneuc

DECOR: A Method for the Specification and Detection of Code and Design Smells

Code and design smells are poor solutions to recurring implementation and design problems. They may hinder the evolution of a system by making it hard for software engineers to carry out changes. We propose three contributions to the research field related to code and design smells: (1) DECOR, a method that embodies and defines all the steps necessary for the specification and detection of code and design smells, (2) DETEX, a detection technique that instantiates this method, and (3) an empirical validation in terms of precision and recall of DETEX. The originality of DETEX stems from the ability for software engineers to specify smells at a high level of abstraction using a consistent vocabulary and domain-specific language for automatically generating detection algorithms. Using DETEX, we specify four well-known design smells: the antipatterns Blob, Functional Decomposition, Spaghetti Code, and Swiss Army Knife, and their 15 underlying code smells, and we automatically generate their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall on XERCES v2.7.0, and discuss the precision of these algorithms on 11 open-source systems.

Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

This paper recasts the problem of feature location in source code as a decision-making problem in the presence of uncertainty. The solution to the problem is formulated as a combination of the opinions of different experts. The experts in this work are two existing techniques for feature location: a scenario-based probabilistic ranking of events and an information-retrieval-based technique that uses latent semantic indexing. The combination of these two experts is empirically evaluated through several case studies, which use the source code of the Mozilla Web browser and the Eclipse integrated development environment. The results show that the combination of experts significantly improves the effectiveness of feature location as compared to each of the experts used independently

An Exploratory Study of the Impact of Code Smells on Software Change-proneness

Code smells are poor implementation choices, thought to make object-oriented systems hard to maintain. In this study, we investigate if classes with code smells are more change-prone than classes without smells. Specifically, we test the general hypothesis: classes with code smells are not more change prone than other classes. We detect 29 code smells in 9 releases of Azureus and in 13 releases of Eclipse, and study the relation between classes with these code smells and class change-proneness. We show that, in almost all releases of Azureus and Eclipse, classes with code smells are more change-prone than others, and that specific smells are more correlated than others to change-proneness. These results justify a posteriori previous work on the specification and detection of code smells and could help focusing quality assurance and testing activities.

An exploratory study of the impact of antipatterns on class change- and fault-proneness

Antipatterns are poor design choices that are conjectured to make object-oriented systems harder to maintain. We investigate the impact of antipatterns on classes in object-oriented systems by studying the relation between the presence of antipatterns and the change- and fault-proneness of the classes. We detect 13 antipatterns in 54 releases of ArgoUML, Eclipse, Mylyn, and Rhino, and analyse (1) to what extent classes participating in antipatterns have higher odds to change or to be subject to fault-fixing than other classes, (2) to what extent these odds (if higher) are due to the sizes of the classes or to the presence of antipatterns, and (3) what kinds of changes affect classes participating in antipatterns. We show that, in almost all releases of the four systems, classes participating in antipatterns are more change-and fault-prone than others. We also show that size alone cannot explain the higher odds of classes with antipatterns to underwent a (fault-fixing) change than other classes. Finally, we show that structural changes affect more classes with antipatterns than others. We provide qualitative explanations of the increase of change- and fault-proneness in classes participating in antipatterns using release notes and bug reports. The obtained results justify a posteriori previous work on the specification and detection of antipatterns and could help to better focus quality assurance and testing activities.

CERBERUS: Tracing Requirements to Source Code Using Information Retrieval, Dynamic Analysis, and Program Analysis

The concern location problem is to identify the source code within a program related to the features, requirements, or other concerns of the program. This problem is central to program development and maintenance. We present a new technique called prune dependency analysis that can be combined with existing techniques to dramatically improve the accuracy of concern location. We developed CERBERUS, a potent hybrid technique for concern location that combines information retrieval, execution tracing, and prune dependency analysis. We used CERBERUS to trace the 360 requirements of RHINO, a 32,134 line Java program that implements the ECMAScript international standard. In our experiment, prune dependency analysis boosted the recall of information retrieval by 155% and execution tracing by 104%. Moreover, we show that our combined technique outperformed the other techniques when run individually or in pairs.

DeMIMA: A Multilayered Approach for Design Pattern Identification

Design patterns are important in object-oriented programming because they offer design motifs, elegant solutions to recurrent design problems, which improve the quality of software systems. Design motifs facilitate system maintenance by helping to understand design and implementation. However, after implementation, design motifs are spread throughout the source code and are thus not directly available to maintainers. We present DeMIMA, an approach to identify semi-automatically micro-architectures that are similar to design motifs in source code and to ensure the traceability of these micro-architectures between implementation and design. DeMIMA consists of three layers: two layers to recover an abstract model of the source code, including binary class relationships, and a third layer to identify design patterns in the abstract model. We apply DeMIMA to five open-source systems and, on average, we observe 34% precision for the considered 12 design motifs. Through the use of explanation-based constraint programming, DeMIMA ensures 100% recall on the five systems. We also apply DeMIMA on 33 industrial components.

Combining Probabilistic Ranking and Latent Semantic Indexing for Feature Identification

The paper recasts the problem of feature location in source code as a decision-making problem in the presence of uncertainty. The main contribution consists in the combination of two existing techniques for feature location in source code. Both techniques provide a set of ranked facts from the software, as result to the feature identification problem. One of the techniques is based on a scenario based probabilistic ranking of events observed while executing a program under given scenarios. The other technique is defined as an information retrieval task, based on the latent semantic indexing of the source code. We show the viability and effectiveness of the combined technique with two case studies. A first case study is a replication of feature identification in Mozilla, which allows us to directly compare the results with previously published data. The other case study is a bug location problem in Mozilla. The results show that the combined technique improves feature identification significantly with respect to each technique used independently

Feature identification: a novel approach and a case study

Feature identification is a well-known technique to identify subsets of a program source code activated when exercising a functionality. Several approaches have been proposed to identify features. We present an approach to feature identification and comparison for large object-oriented multi-threaded programs using both static and dynamic data. We use processor emulation, knowledge filtering, and probabilistic ranking to overcome the difficulties of collecting dynamic data, i.e., imprecision and noise. We use model transformations to compare and to visualise identified features. We compare our approach with a naive approach and a concept analysis-based approach using a case study on a real-life large object-oriented multi-threaded program, Mozilla, to show the advantages of our approach. We also use the case study to compare processor emulation with statistical profiling.

Recovering binary class relationships: putting icing on the UML cake

A discontinuity exists between object-oriented modeling and programming languages. This discontinuity arises from ambiguous concepts in modeling languages and a lack of corresponding concepts in programming languages. It is particularly acute for binary class relationships---association, aggregation, and composition. It hinders the traceability between software implementation and design, thus hampering software analysis. We propose consensual definitions of the binary class relationships with four minimal properties---exclusivity, invocation site, lifetime, and multiplicity. We describe algorithms to detect automatically these properties in source code and apply these on several frameworks. Thus, we bridge the gap between implementation and design for the binary class relationships, easing software analysis.

AURA: a hybrid approach to identify framework evolution

Software frameworks and libraries are indispensable to todays software systems. As they evolve, it is often time-consuming for developers to keep their code up-to-date, so approaches have been proposed to facilitate this. Usually, these approaches cannot automatically identify change rules for one-replaced-by-many and many-replaced-by-one methods, and they trade off recall for higher precision using one or more experimentally-evaluated thresholds. We introduce AURA, a novel hybrid approach that combines call dependency and text similarity analyses to overcome these limitations. We implement it in a Java system and compare it on five frameworks with three previous approaches by Dagenais and Robillard, M. Kim et al., and Schäfer et al. The comparison shows that, on average, the recall of AURA is 53.07% higher while its precision is similar, e.g., 0.10% lower.