On the Interplay between Structural and Logical Dependencies in Open-Source Software

Oliva, Gustavo Ansaldi; Gerosa, Marco Aurélio

doi:10.1109/sbes.2011.39

Cited by 21 publications

(47 citation statements)

References 31 publications

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“…A dependency is a semantic relationship that indicates that a client element may be affected by changes performed in a supplier element (Oliva and Gerosa 2011). In Sections 2.1.1 and 2.1.2, we introduce semantic and logical dependencies and discuss how they can be operationalised in an OO context.…”

Section: Oo Software Dependenciesmentioning

confidence: 99%

“…Therefore, the logical coupling of any two classes is based on their change history, and is a measure of the observation that the two classes always co-evolve or change together (Gal et al 1998(Gal et al , 2003D'Ambros et al 2009;. They are commonly treated as association rules (Zimmermann et al 2005), which means that when X 1 is changed, X 2 is also changed (Oliva and Gerosa 2011). Furthermore, X1 and X2 are called the antecedent (i.e., left-hand-side, LHS) and the consequent (i.e., right-hand-side, RHS) of the rule, respectively.…”

Section: Logical Couplingmentioning

confidence: 99%

“…Furthermore, X1 and X2 are called the antecedent (i.e., left-hand-side, LHS) and the consequent (i.e., right-hand-side, RHS) of the rule, respectively. For example, the rule {A, B}→ C found in the sales data of a supermarket indicates that a customer who buys A and B together, is also likely to buy C (Oliva and Gerosa 2011). Two classes change at the same time when changes in one class A are made in response to a change in another class B. Kagdi et al (2013) state that logical coupling captures the extent to which software artefacts co-evolve and this information is derived by analysing patterns, relationships and relevant information of source code changes mined from multiple versions (of software systems) in software repositories (e.g., Subversion and Bugzilla).…”

Section: Logical Couplingmentioning

confidence: 99%

“…By doing so, we evaluated the significance of the association rules between classes (Oliva and Gerosa 2011), and across the lifespan of a software project (i.e., taking all versions of the software system into consideration).…”

Section: Operationalisationmentioning

confidence: 99%

“…Other studies, specifically addressed at establishing a link between coupling and co-change, have found that the set of co-changed classes was much larger compared to the set of structurally coupled classes Gerosa 2011, 2015;Fluri et al 2005, Geipel andSchweitzer 2012). This implies that not all of the change dependencies are related to structural dependencies and there could be other reasons for software artefacts to be change dependent (Oliva and Gerosa 2011). High coupling between classes in an OO design can increase system complexity by introducing multiple inter-dependencies among the classes (Subramanyam and Krishnan 2003).…”

Section: Introductionmentioning

confidence: 98%

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An empirical study on the interplay between semantic coupling and co-change of software classes

2017

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Software systems continuously evolve to accommodate new features and interoperability relationships between artifacts point to increasingly relevant software change impacts. During maintenance, developers must ensure that related entities are updated to be consistent with these changes. Studies in the static change impact analysis domain have identified that a combination of source code and lexical information outperforms using each one when adopted independently. However, the extraction of lexical information and the measure of how loosely or closely related two software artifacts are, considering the semantic information embedded in their comments and identifiers has been carried out using somewhat complex information retrieval (IR) techniques. The interplay between software semantic and change relationship strengths has also not been extensively studied. This work aims to fill both gaps by comparing the effectiveness of measuring semantic coupling of OO software classes using (i) simple identifier based techniques and (ii) the word corpora of the entire classes in a software system. Afterwards, we empirically investigate the interplay between semantic and change coupling. The empirical results show that: (1) identifier based methods have more computational efficiency but cannot always be used interchangeably with corpora-based methods of computing semantic coupling of classes and (2) there is no correlation between semantic and change coupling. Furthermore we found that (3) there is a directional relationship between the two, as over 70% of the semantic dependencies are also linked by change coupling but not vice versa.

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Section: Oo Software Dependenciesmentioning

confidence: 99%

Section: Logical Couplingmentioning

confidence: 99%

Section: Logical Couplingmentioning

confidence: 99%

Section: Operationalisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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An empirical study on the interplay between semantic coupling and co-change of software classes

2017

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An empirical analysis of source code metrics and smart contract resource consumption

Ajienka

Vangorp

Capiluppi

2020

J Software Evolu Process

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A smart contract (SC) is a programme stored in the Ethereum blockchain by a contract-creation transaction. SC developers deploy an instance of the SC and attempt to execute it in exchange for a fee, paid in Ethereum coins (Ether). If the computation needed for their execution turns out to be larger than the effort proposed by the developer (i.e., the gasLimit), their client instantiation will not be completed successfully. In this paper, we examine SCs from 11 Ethereum blockchain-oriented software projects hosted on GitHub.com, and we evaluate the resources needed for their deployment (i.e., the gasUsed). For each of these contracts, we also extract a suite of object-oriented metrics, to evaluate their structural characteristics. Our results show a statistically significant correlation between some of the object-oriented (OO) metrics and the resources consumed on the Ethereum blockchain network when deploying SCs. This result has a direct impact on how Ethereum developers engage with a SC: evaluating its structural characteristics, they will be able to produce a better estimate of the resources needed to deploy it. Other results show specific source code metrics to be prioritised based on application domains when the projects are clustered based on common themes. KEYWORDS abstract syntax-tree (AST), blockchain-oriented software (BOS), Chidamber and Kemerer (C&K), object-oriented (OO), object-oriented programming (OOP), smart contract (SC) 1 INTRODUCTION A blockchain is a shared ledger that stores transactions in a decentralised 1 peer-to-peer network of computers also known as nodes. Blockchain transactions can be composed of contract creation transactions and contract function invoking transactions. The former deploys and records a smart contract (SC) on the blockchain, and the latter causes the execution of a contract functionality. 2,3 The third transaction type is the token or cryptocurrency transfer transaction such as Bitcoin transfers on the Bitcoin Blockchain or Ether transfers on the Ethereum Blockchain. As a whole, the blockchain technology provides a decentralised, trustless platform that combines transparency, immutability and consensus properties to enable secure, pseudo-anonymous transactions. SCs are the programmes stored in a blockchain by a contract-creation transaction. In the last few years, SCs have been used in different scenarios: in voting platforms to secure votes; to automatically process insurance claims according to agreed terms and postal companies for payments on delivery. 5 Porru et al 6 defined the term blockchain-oriented software (BOS) as a software that contributes to the realization of a blockchain project. This definition includes both blockchain platforms (or networks), such as Bitcoin and Ethereum, and general blockchain software commonly referred to as decentralised apps (DApps). 7 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work...

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An Empirical Study of the Relation Between Strong Change Coupling and Defects Using History and Social Metrics in the Apache Aries Project

Wiese

Kuroda

Ré

et al. 2015

Open Source Systems: Adoption and Impact

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Part 1: Open Source Software EngineeringInternational audienceChange coupling is an implicit relationship observed when artifacts change together during software evolution. The literature leverages change coupling analysis for several purposes. For example, researchers discovered that change coupling is associated with software defects and reveals relationships between software artifacts that cannot be found by scanning code or documentation. In this paper, we empirically investigate the strongest change couplings from the Apache Aries project to characterize and identify their impact in software development. We used historical and social metrics collected from commits and issue reports to build classification models to identify strong change couplings. Historical metrics were used because change coupling is a phenomenon associated with recurrent co-changes found in the software history. In turn, social metrics were used because developers often interact with each other in issue trackers to accomplish the tasks. Our classification models showed high accuracy, with 70−99 % F-measure and 88−99 % AUC. Using the same set of metrics, we also predicted the number of future defects for the artifacts involved in strong change couplings. More specifically, we were able to predict 45.7 % of defects where these strong change couplings reoccurred in the post-release. These findings suggest that developers and projects managers should detect and monitor strong change couplings, because they can be associated with defects and tend to happen again in the subsequent release

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On the Interplay between Structural and Logical Dependencies in Open-Source Software

Cited by 21 publications

References 31 publications

An empirical study on the interplay between semantic coupling and co-change of software classes

An empirical study on the interplay between semantic coupling and co-change of software classes

An empirical analysis of source code metrics and smart contract resource consumption

An Empirical Study of the Relation Between Strong Change Coupling and Defects Using History and Social Metrics in the Apache Aries Project

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