Large Language Models (LLMs) are increasingly integrated into software development workflows, yet their effects are often discussed without distinguishing between task types, developer seniority, and verification demands. This paper presents a mixed-method empirical study of LLM-assisted software engineering with first-year and fourth-year undergraduates. Phase 1 is a preliminary survey (n=157) that characterizes LLM exposure, reliance, and trust calibration among the two groups. Phase 2 is a task-based quasi-experiment with a purposive sample from both cohorts (n=20). Here, we compare AI-assisted and non-AI conditions on a structured set of software engineering tasks spanning implementation, constraint-driven algorithm selection, and architectural reasoning. We then analyze performance outcomes alongside behavioral traces captured via screen recording and a qualitative coding process. Survey results indicate widespread LLM adoption and substantial verification effort, alongside cohort differences in perceived LLM capability for constraint-heavy scenarios. The quasi-experiment further shows that AI assistance changes workflow structure. For example, participants frequently adopt AI-first task entry, copy-transfer integration, and AI-mediated debugging, whereas non-AI workflows rely more on documentation, prior templates, and iterative trial-error refinement. Overall, our findings suggest that the benefits of LLM assistance are task-dependent and mediated by expertise and verification practices, rather than by generation speed alone.

ABSTRACT Effective coordination between contributors with different functional roles is fundamental for the success of collaboration‐centric software development paradigms such as DevSecOps. However, quantitatively assessing coordination in such settings has received limited attention. We introduce multi‐class socio‐technical congruence (), an extension of the widely studied socio‐technical congruence () framework to address this gap. Our metric enables the assessment of coordination in a setting where contributors with different functional roles or alignments collaborate. Using a large‐scale exploratory case study, we evaluated for two classes (i.e., ). Specifically, we calculated for 100 systematically selected projects from the TravisTorrent dataset, considering developers (dev) and security‐focused developers (sf‐devs) as the two types of contributors with different functional alignments (i.e., two classes). We hypothesized that the dev and sf‐dev interaction would have a quantifiable impact on the vulnerability score () of each project. Our results show a moderate negative association between and , with the Spearman correlation reaching 0.427 (), indicating that higher levels of coordination between dev and sf‐dev led to projects with a lower incidence of high‐severity vulnerabilities. In addition, shows a stronger negative relationship with than , suggesting that it is the more sensitive indicator of this relationship. Therefore, the specific instantiation of our proposed metric, , performs comparatively better than for measuring cross‐functional coordination in our selected projects. However, further research is needed to explore its broader applicability.

Background: Security tools play a vital role in enabling developers to build secure software. However, it can be quite challenging to introduce and fully leverage security tools without affecting the speed or frequency of deployments in the DevOps paradigm. Aims: We aim to empirically investigate the key challenges practitioners face when integrating security tools into a DevOps workflow in order to provide recommendations for overcoming the challenges. Method: We conducted a study involving 31 systematically selected webinars on integrating security tools in DevOps. We used a qualitative data analysis method, i.e., thematic analysis, to identify the challenges and emerging solutions related to integrating security tools in rapid deployment environments. Results: We find that whilst traditional security tools are unable to cater for the needs of DevOps, the industry is moving towards new generations of security tools that have started focusing on the needs of DevOps. We have developed a DevOps workflow that integrates security tools and a set of guidelines by synthesizing practitioners' recommendations in the analyzed webinars. Conclusion: Whilst the latest security tools are addressing some of the requirements of DevOps, there are many tool-related drawbacks yet to be adequately addressed.

Context: With the growing popularity of rapid software delivery and deployment, the methods, practices and technologies of Continuous Software Engineering (CSE) are evolving steadily. This creates the need for understanding the recent trends of the technologies, practitioners' challenges and views in this domain. Objective: In this paper, we present an empirical study aimed at exploring CSE from the practitioners' perspective by mining discussions from Q&A websites. Method: We have analyzed 12,989 questions and answers posted on Stack Overflow. Topic modelling is conducted to derive the dominant topics in this domain. Further, a qualitative analysis was conducted to identify the key challenges discussed. Findings: Whilst the trend of posted questions is sharply increasing, the questions are becoming more specific to technologies and more difficult to attract answers. We identified 32 topics of discussions, among which "Error messages in Continuous Integration/Deployment" and "Continuous Integration concepts" are the most dominant. We also present the most challenging areas in this domain from the practitioners' perspectives.