Context: The vast majority of software engineering research is reported independently of the application domain: techniques and tools usage is reported without any domain context. As reported in previous research, this has not always been so: early in the computing era, the research focus was frequently application domain specific (for example, scientific and data processing).

Abstract 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.

The evolution of software systems is an inevitable process which has to be managed effectively to enhance software quality. Change impact analysis (CIA) is a technique that identifies impact sets, i.e., the set of classes that require correction as a result of a change made to a class or artefact. These sets can also be considered as ripple effects and typically non-local: changes propagate to different parts of a system.

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.

Dependency-based software change impact analysis is the domain concerned with estimating the sets of artifacts impacted by a change to a related artifact. Research has shown that analysing the various class dependency types independently will never completely reveal the impact sets. Therefore, dependency types are combined to improve the precision of estimated when compared to impact sets. Software classes can be linked in different ways; for instance semantically, if their meaning is somewhat related or, structurally, if one class depends on the services of other classes. 'Hidden' dependencies arise when two classes, linked structurally, do not share the same semantic namespace or when semantically dependent classes do not share a structural link. With the goal of revealing hidden dependencies during change impact analysis, we empirically investigated the relationship between structural and semantic class dependencies in object-oriented software systems. Results show that (i) semantic and structural links are significantly associated, (ii) the strengths of those links do not play a significant role and, (iii) a significant number of dependencies are hidden. We propose two refactoring techniques to deal with hidden dependencies, based on existing design patterns. We plan to investigate them further to assert whether either has the potential for reducing refactoring and testing effort.

Context: Conceptual coupling is a measure of how loosely or closely related two software artifacts are, by considering the semantic information embedded in the comments and identifiers. This type of coupling is typically evaluated using the semantic information from source code into a words corpus. The extraction of words corpora can be lengthy, especially when systems are large and many classes are involved.