Software, System, and Service Engineering

Software environments have become increasingly sophisticated in recent years, giving rise to modern measuring instruments that use simple hardware sensors but defined with complex software. The conformity of such devices in the legally regulated sector is usually ensured by using version numbers or hashes over executable binaries, which is inefficient due to the sensitivity of hashes to even small changes in the code. However, the legal requirements could also be equally satisfied if the certified prototype and devices in field possess identical functional behavior, even when hashes differ. With such functional identification, the device manufacturers could gain the opportunity to introduce software patches or bugfixes without having to go through the complete mandatory certification process again. Based on the \(L^*\) algorithm, which learns the accepted language and hence the internal state model of the instrument software represented by deterministic finite automata, a risk-based method is proposed to realize automatic functional identification of software to a certain extent, thereby allowing continuous quality control of periodically updated measuring instruments without frequent manual certifications. Risk assessment is used to identify critical modifications introduced in monitored devices, which then triggers warnings for manual inspections when necessary. This article is an extended version of the work previously published in [7] and is envisioned to be a first step to realize fully automatic quality control for regulated measuring instruments.

Requirement specifications play an essential role in software development processes. They can take different forms, including a use-case model. The use-case model defines the use-cases and the relationships among them and contains definitions of the use-case specifications. It is used to estimate software development project efforts and for planning iterations. The use-case model is subject to change as requirements evolve or the model is refactored. Therefore, it is essential that the use-case model is not redundant and its parts are reusable. Existing approaches for the use-case model specification support reusability in a limited way. This paper fills the gap. It introduces a new Use-Case Flow Language to specify the entire use-case model conveniently yet semi-formally. The language is defined at the abstract syntax level by a metamodel with an informal description of the semantics of the metamodel elements. A discussion and justification of metamodel elements is given. A concrete textual syntax of the language is also provided and informally described. An example of a use-case model specified in the proposed notation is presented in the paper.

Psychological safety, a pivotal factor in team dynamics, has been proposed as a crucial determinant of success in agile software development (ASD) teams and learning. However, the extent of its influence within the domain of large-scale agile (LSA) software development teams remains underexplored. This research investigates the multifaceted dimensions of psychological safety within LSA teams, examining both its precursors and outcomes. This study conducted semi-structured interviews with software professionals actively involved in LSA projects within a Swedish software consultancy company. The findings underscore the intricate nature of establishing a psychologically safe environment within LSA teams, revealing it as a multidimensional construct necessitating a proactive leadership approach, fostering open communication, and cultivating an ecosystem of constructive feedback. The study highlights the critical importance of intentionally shaping teams to facilitate continuous learning, ensuring remuneration safety, and implementing a comprehensive onboarding process for incoming team members. By fostering psychologically safe settings, LSA teams enhanced teamwork dynamics, heightened job satisfaction, and facilitation continuous learning and development. Notably, the absence of such an environment exacerbates the phenomenon of brain drain, exposing the tangible consequences of overlooking this fundamental aspect of organizational culture. This study proposes avenues for future research directions, aiming to further unravel the nuances of psychological safety and its cascading effects within the realm of contemporary LSA software development context.

Selecting suitable requirements elicitation, specification, and modeling techniques in IT projects is crucial to the business analysis planning process. Typically, the determining factors are the preferences of stakeholders, primarily business analysts, previous experience, and company practices, as well as the availability of sources of information and tools. The influence of other factors is not as evident. One viable method for generating guidance on technique utilization involves the examination of industrial expertise. The primary objective of this research is to investigate the utilization of association rules mining in order to delineate the variables that impact the selection of requirements elicitation and analysis techniques and to forecast the use of specific techniques contingent upon the project’s context and the business analyst’s profile. Three hundred twenty-eight practitioners from Ukraine’s IT industry were surveyed regarding their current practices in business analysis to form a dataset for experiments. The found associations give the potential to expedite the technique selection process in requirement management and enhance the overall efficiency of the business analysis activities.

Enterprise systems are inherently complex and maintaining their full, up-to-date overview poses a serious challenge to the enterprise architects’ teams. This problem encourages the search for automated means of discovering knowledge about such systems. An important aspect of this knowledge is understanding the data that are processed by applications and their relationships. In our previous work, we used application logs of an enterprise system to derive knowledge about the interactions taking place between applications. In this paper, we further explore logs to discover correspondence between data processed by different applications. Our contribution is the following: we propose a method for discovering relationships between data using log analysis, we validate our method against a real-life system running at Nordea Bank, we provide detailed insights into a real-life dataset, we analyze the influence of log quality on the results provided by our method, and we provide recommendations for developers on logging practices that can support the log analysis.

Metaprogramming, the practice of writing programs that manipulate other programs at compile-time, continues to impact software development; enabling new approaches to optimisation, static analysis, and reflection. Nevertheless, a challenge associated with metaprogramming techniques, including C++ constexpr functionality, is an increase in compilation times. This paper presents ClangOz, a novel Clang-based research compiler that addresses this issue by evaluating annotated constant expressions in parallel, thereby reducing compilation time.