Increasing use of artificial intelligence tools in programming education calls for a deeper understanding of their effect on students’ learning. This paper presents a study that investigates the experiences of part-time undergraduate students using ChatGPT in a five-week Java programming course. After each exercise, students provided feedback via anonymous surveys in which they rated different suitability aspects of ChatGPT. The majority viewed ChatGPT positively and suitable for learning programming concepts. However, its suitability for specific implementation tasks received mixed reviews. Students found it easy to adapt ChatGPT’s generated code to the exercises’ implementation tasks. The students primarily used it for acquiring background knowledge, learning syntax and programming concepts and suggesting suitable algorithms. Yet, some abstained from using it due to concerns to not garner sufficient programming proficiency, retrieving partially incorrect or misleading generated code, preferring an independent working style, or general skepticism about its benefits. Finally, in response to our findings, we also discuss three perspective directions for improving the suitability of LLM chatbots for students in programming education.

Ensuring security is of paramount importance in microservice architectures, given their distributed nature, involving numerous services and network-spanning interactions. This architectural style, which can comprise hundreds to thousands of services, inherently presents a more extensive attack surface compared to traditional monolithic applications. Moreover, the polyglot nature of microservices, which encompasses services developed and deployed using diverse programming languages and technologies, further complicates the security landscape. This paper presents a systematic literature review, analyzing 54 publications specifically in the context of security threats and mitigation strategies within the area of inter-service security in microservice architectures. We observed that the majority of studies focus on presenting methods, models, and guidelines for microservice security, with a significant portion validating these approaches. Publications in the field have increased since 2015, with conference papers being the most common type. Security threats identified are mainly related to security perimeters, attack surfaces, and inadequate monitoring and intrusion detection. There is a notable lack of comprehensive analysis on specific security threats, particularly in inter-service authentication and communication. Mitigation strategies receive more attention than security threats, with extensive discussion on infrastructure defense and secure coding practices. The identified research gap highlights the need for establishing a connection between security threats and their mitigation strategies in microservice architectures. It also underscores the necessity for a standardized taxonomy in microservice security to clarify terminology and consolidate best practices, addressing inconsistencies in the literature and guiding future empirical studies on the practical challenges of implementing security measures.

In the realm of AI-enhanced programming education, there is growing interest in using such tools to help students understand good coding principles. This study investigates the impact of ChatGPT on code quality among part-time undergraduate students in introductory Java programming courses, who lack prior Java experience. The source code of 16 students from the control group (without ChatGPT) and 22 students from the treatment group (with ChatGPT) who completed identical programming exercises focused on coding conventions was analyzed. Static code analysis tools assessed adherence to a common coding convention ruleset and calculated cyclomatic and cognitive complexity metrics. The comparative analysis shows that the ChatGPT-assisted group significantly improved code quality, with fewer rule violations and reduced cyclomatic and cognitive complexities. The treatment group adhered more closely to coding standards and produced less complex code. Violations primarily occurred in line length, final parameters, and the extensibility of object-oriented programming (OOP). These findings suggest that ChatGPT can be beneficial in programming education by helping students write cleaner, less complex code and adhere to coding conventions. However, the study’s limitations, such as the small sample size and novice status of participants, call for further research with larger, more diverse populations and different educational contexts.

Architecture decision records (ADRs) have been proposed as a resource-efficient means for capturing architectural design decisions (ADDs), and have received attention not only from researchers but also from practitioners. We conducted a mining software repositories (MSR) study, in which we analyzed the use of ADRs in open source repositories at GitHub. Our results show that the adoption of ADRs is still low, although the number of repositories using ADRs is increasing every year. About 50% of all repositories with ADRs contain just one to five ADRs suggesting that the concept has been tried but not yet definitively adopted. In repositories that use ADRs more systematically, we observed that recording decisions is a team activity conducted by two or more users over a longer period of time. In most repositories the template proposed by Michael Nygrad is used. We, finally, provide an interpretation of the obtained results and discuss open future research challenges by elaborating on implications of the study’s findings as well as on recommendations on how to further increase the adoption of ADRs.

When following the principles of value‐based software engineering, business, customer satisfaction, and engineering considerations need to be balanced to develop and operate the software so that it satisfies the different stakeholders' expectations. This, however, requires knowing the relevant quality metrics covering these value‐oriented expectations and potential sources for their measurement. In this work, a categorisation of value‐oriented quality metrics that are practically relevant is presented. Therefore, the authors conducted an online survey with practitioners who assessed the relevance of 61 value‐oriented metrics, gathered from a preceding systematic mapping study. The authors grouped these metrics into 10 categories, based on financial, customer satisfaction, value proposition, and creation perspectives. Also, the authors examined the frequency of particular steps at which these measures accrue and identified their most relevant data sources. The participants rated metrics for feature reliability, performance, as well as test and development efficiency as most relevant for value orientation. According to the participants, the authors' collection covers all relevant metrics for addressing financial and market and feature usability aspects. The authors' categorisation and the metrics' relevance assessments shall support software engineers in selecting relevant metrics and their sources for software product development.