Analysing Anti-patterns Static Relationships with Design Patterns

Anti-patterns are motifs that are usually thought to be good solutions tosome design or implementation problems, but back-fires badly when applied. Previousstudies have reported that anti-patterns make object oriented systems hard tomaintain. Anti-patterns motifs usually have dependencies with other classes in thesystem. In this paper, we propose to analyse these dependencies (with in particulardesign patterns) in order to understand how developers can maintain programscontaining anti-patterns. To the best of our knowledge, no substantial investigationof anti-pattern dependencies with design patterns has been done before. This paperpresents the results of a study that we performed on three different systems, ArgoUML,JFreeChart, and XercesJ, written in Java, and of size ranges from 1,191to 3,325 classes, to analyse the static relationships between anti-patterns and designpatterns. We found that these relationships (1) exist, but (2) are temporaryand (3) classes participating in such relationships are more change-prone but lessfault-prone than other anti-pattern classes

Linguistic Anti-Patterns: Impact Analysis on Code Quality

Les “mauvaises odeurs” de conception sont des structures qui indiquent une violation des principes fondamentaux de conception et qui nuisent à la qualité des systèmes logiciels. Ils représentent des choix d’architectures, de conception, et d’implémentation qui doivent être suivis et améliorés. Dans ce travail, on considère deux sous types de ces “mauvaises odeurs” qui sont les anti-patrons de conception (DAPs) et les anti-patrons linguistiques (LAs). Les anti-patrons de conception (DAPs) sont les patrons que les développeurs considèrent comme étant des bonnes solutions à certains problèmes mais qui ont en réalité un impact négatif sur la qualité des logiciels. Des études récentes ont démontré que les anti-patrons rendent la maintenance logicielle plus difficile dans les systèmes orientés objets ainsi qu’ils augmentent le changement et les défaillances. Le concept d’anti-patrons linguistiques (LAs) fait référence aux mauvaises pratiques de nommage, de documentation et de l’implémentation du code source qui peuvent négativement impacter la qualité des systèmes logiciels et la compréhension du programme. Contrairement aux anti-patrons, les patrons de conception (DPs) présentent une solution prometteuse qui sert à améliorer la qualité des systèmes orientés objets. Dans certains cas, les patrons de conception et contrairement à ce qui est connu, peuvent avoir aussi un impact négatif sur la qualité logicielle. Pour cela, nous considérons également les patrons de conception dans ce travail afin d’étudier leurs comportements et leurs qualité au cours de l’évolution des logiciels. Avoir une bonne qualité logicielle est primordial pour contrôler et réduire les coûts de la maintenance des systèmes orientés objets. Il est important de disposer de mécanismes permettant de mesurer la qualité logicielle. Cependant, la qualité a des différentes significations qui peuvent être par exemple la capacité d’un système à changer à faible coût ou même l’absence de bogues dans le logiciel. Dans cette thèse, nous considérons comme mesures indirectes de la qualité: la compréhension du code, la propension au changement, et la prévalance de fautes. Au cours de l’évolution d’un logiciel, les développeurs risquent d’introduire des anti-patrons durant leurs tâches de développement (fixer des bogues, ajouter des nouvelles fonctionnalités, ou même appliquer des nouvelles exigences). Dans cette thèse, nous avons étudié l’impact des anti-patrons de conception, les anti-patrons linguistiques, et les patrons de conception sur la qualité logicielle. ----------ABSTRACT: Design smells are bad practices in software design that lower the quality of software systems. They represent architectural, design, and implementation choices that should be tracked and removed. We consider design anti-patterns (DAPs) and linguistic anti-patterns (LAs) as two special types of design smells in our work, in contrast to design patterns (DPs). DAPs are software patterns that are thought by developers to be good solutions to some design problems but that have actually a negative impact on quality. Recent studies have brought evidence that DAPs make maintenance more difficult in object-oriented systems and increase change- and fault-proneness. LAs refer to bad practices of naming, documentation, and implementation of code entities, which could decrease the quality of software systems and have a negative impact on program comprehension. Opposite to design smells, DPs are promising solutions to improve the quality of object-oriented systems. Yet against popular wisdom, design patterns in practice can impact quality negatively. Achieving good quality is important to control and reduce the maintenance cost of object-oriented systems. This goal requires means to measure the quality of systems. However, quality has different meanings, e.g., the capacity of a system to change at low cost or the absence of bugs. In this thesis, we consider code understanding, change-proneness, and fault-proneness as three proxy measures for quality

Smart Contracts Software Metrics: a First Study

© 2018 The Author(s).Smart contracts (SC) are software codes which reside and run over a blockchain. The code can be written in different languages with the common purpose of implementing various kinds of transactions onto the hosting blockchain, They are ruled by the blockchain infrastructure and work in order to satisfy conditions typical of traditional contracts. The software code must satisfy constrains strongly context dependent which are quite different from traditional software code. In particular, since the bytecode is uploaded in the hosting blockchain, size, computational resources, interaction between different parts of software are all limited and even if the specific software languages implement more or less the same constructs of traditional languages there is not the same freedom as in normal software development. SC software is expected to reflect these constrains on SC software metrics which should display metric values characteristic of the domain and different from more traditional software metrics. We tested this hypothesis on the code of more than twelve thousands SC written in Solidity and uploaded on the Ethereum blockchain. We downloaded the SC from a public repository and computed the statistics of a set of software metrics related to SC and compared them to the metrics extracted from more traditional software projects. Our results show that generally Smart Contracts metrics have ranges more restricted than the corresponding metrics in traditional software systems. Some of the stylized facts, like power law in the tail of the distribution of some metrics, are only approximate but the lines of code follow a log normal distribution which reminds of the same behavior already found in traditional software systems.Submitted Versio

EMPIRICAL ASSESSMENT OF THE IMPACT OF USING AUTOMATIC STATIC ANALYSIS ON CODE QUALITY

Automatic static analysis (ASA) tools analyze the source or compiled code looking for violations of recommended programming practices (called issues) that might cause faults or might degrade some dimensions of software quality. Antonio Vetro' has focused his PhD in studying how applying ASA impacts software quality, taking as reference point the different quality dimensions specified by the standard ISO/IEC 25010. The epistemological approach he used is that one of empirical software engineering. During his three years PhD, he's been conducting experiments and case studies on three main areas: Functionality/Reliability, Performance and Maintainability. He empirically proved that specific ASA issues had impact on these quality characteristics in the contexts under study: thus, removing them from the code resulted in a quality improvement. Vetro' has also investigated and proposed new research directions for this field: using ASA to improve software energy efficiency and to detect the problems deriving from the interaction of multiple languages. The contribution is enriched with the final recommendation of a generalized process for researchers and practitioners with a twofold goal: improve software quality through ASA and create a body of knowledge on the impact of using ASA on specific software quality dimensions, based on empirical evidence. This thesis represents a first step towards this goa

Defining linguistic antipatterns towards the improvement of source code quality

Previous studies showed that linguistic aspect of source code is a valuable source of information that can help to improve program comprehension. The proposed research work focuses on supporting quality improvement of source code by identifying, specifying, and studying common negative practices (i.e., linguistic antipatterns) with respect to linguistic information. We expect the definition of linguistic antipatterns to increase the awareness of the existence of such bad practices and to discourage their use. We also propose to study the relation between negative practices in linguistic information (i.e., linguistic antipatterns) and negative practices in structural information (i.e., design antipatterns) with respect to comprehension effort and fault/change proneness. We discuss the proposed methodology and some preliminary results

Automatically Identifying Code Features for Software Defect Prediction: Using AST N-grams

Context: Identifying defects in code early is important. A wide range of static code metrics have been evaluated as potential defect indicators. Most of these metrics offer only high level insights and focus on particular pre-selected features of the code. None of the currently used metrics clearly performs best in defect prediction. Objective: We use Abstract Syntax Tree (AST) n-grams to identify features of defective Java code that improve defect prediction performance. Method: Our approach is bottom-up and does not rely on pre-selecting any specific features of code. We use non-parametric testing to determine relationships between AST n-grams and faults in both open source and commercial systems. We build defect prediction models using three machine learning techniques. Results: We show that AST n-grams are very significantly related to faults in some systems, with very large effect sizes. The occurrence of some frequently occurring AST n-grams in a method can mean that the method is up to three times more likely to contain a fault. AST n-grams can have a large effect on the performance of defect prediction models. Conclusions: We suggest that AST n-grams offer developers a promising approach to identifying potentially defective code