Abstract Quantum computing is a promising field that can solve complex problems beyond traditional computers’ capabilities. Developing high-quality quantum software applications, called quantum software engineering, has recently gained attention. However, quantum software development faces challenges related to code quality. A recent study found that many open-source quantum programs are affected by quantum-specific code smells, with long circuit being the most common. While the study provided relevant insights into the prevalence of code smells in quantum circuits, it did not explore the potential effect of transpilation, a necessary step for executing quantum computer programs, on the emergence of code smells. Indeed, transpilation might alter those characteristics employed to detect the presence of a smell on a circuit. To address this limitation, we present a new study investigating the impact of transpilation on quantum-specific code smells and how different target gate sets affect the results. We conducted experiments on 17 open-source quantum programs alongside a set of 100 synthetic circuits. We found that transpilation can significantly alter the metrics that are used to detect code smells, even into previously smell-free circuits, with the long circuit smell being the most susceptible to transpilation. Furthermore, the choice of the gate set significantly influences the presence and severity of code smells in transpiled circuits, highlighting the need for careful gate set selection to mitigate their impact. These findings have implications for circuit optimization and high-quality quantum software development. Further research is needed to understand the consequences of code smells and their potential impact on quantum computations, considering the characteristics and constraints of different gate sets and hardware platforms.

Quantum computing is becoming a reality, and quantum software engineering (QSE) is emerging as a new discipline to enable developers to design and develop quantum programs. This paper presents a systematic mapping study of the current state of QSE research, aiming to identify the most investigated topics, the types and number of studies, the main reported results, and the most studied quantum computing tools/frameworks. Additionally, the study aims to explore the research community’s interest in QSE, how it has evolved, and any prior contributions to the discipline before its formal introduction through the Talavera Manifesto. We searched for relevant articles in several databases and applied inclusion and exclusion criteria to select the most relevant studies. After evaluating the quality of the selected resources, we extracted relevant data from the primary studies and analyzed them. We found that QSE research has primarily focused on software testing, with little attention given to other topics, such as software engineering management. The most commonly studied technology for techniques and tools is Qiskit, although, in most studies, either multiple or none specific technologies were employed. The researchers most interested in QSE are interconnected through direct collaborations, and several strong collaboration clusters have been identified. Most articles in QSE have been published in non-thematic venues, with a preference for conferences. The study’s implications are providing a centralized source of information for researchers and practitioners in the field, facilitating knowledge transfer, and contributing to the advancement and growth of QSE.

Software maintenance is the software life cycle's longest and most challenging phase. Bad architectural decisions or sub-optimal solutions might lead to architectural erosion, i.e., the process that causes the system's architecture to deviate from its original design. The so-called architectural smells are the most common signs of architectural erosion. Architectural smells might affect several quality aspects of a software system, including testability. When a system is not prone to testing, sub-optimal solutions may be introduced in the test code, a.k.a. test smells. This paper explores the possible relations between architectural and test smells. By mining 798 releases of 40 open-source Java systems, we studied the correlation between class-level architectural and test smells. In particular, Eager Test and Assertion Roulette smells often occur in conjunction with Cyclically-dependent Modularization, Deficient Encapsulation, and Insufficient Encapsulation architectural smells.

During software evolution, code is inevitably subject to continuous changes that are often performed by developers within short and strict deadlines. As a consequence, good design practices are often sacrificed, possibly leading to the introduction of sub-optimal design or implementation solutions, the so-called code smells. Several studies have shown that the presence of code smells makes the source code more change- and fault-prone, reduces productivity, and causes greater rework and more significant design efforts for developers. Refactoring is the practice that developers may use to remove code smells without changing the external behavior of the source code. However, it requires much time and effort and is poorly automated, often leading developers to prefer keeping low-quality code instead of spending time in designing and performing refactoring operations. To mitigate this problem and support developers throughout the process of code smell identification and refactoring, in this paper we present cASpER, a IntelliJ IDEA plugin that provides visual and semi-automatic support for detection and refactoring four different types of code smells.

Software development is known to be a social activity that involves developers, project managers, and stakeholders. Recent studies have proved a direct relation between social and technical aspects, e.g., poor coordination among developers may lead to an increase of technical debt in source code. The so-called socio-technical congruence measures the level of coordination existing in an organization at their different levels. In this late-breaking idea paper, we propose a novel way to employ the socio-technical congruence in the context of source code quality improvement: we design a community-based refactoring recommendation approach that aims at optimizing socio-technical congruence while keeping into account the source code dependencies among the components of a software project. A search-based algorithm is employed to this purpose and we envision the novel approach to be suitable for providing Extract Class and Extract Package refactoring recommendations.