261 research outputs found
Exploring Engagement in Hybrid Meetings
No full textBackground. The widespread adoption of hybrid work following the COVID-19 pandemic has fundamentally transformed software development practices, introducing new challenges in communication and collaboration as organizations transition from traditional office-based structures to flexible working arrangements. This shift has established a new orga- nizational norm where even traditionally office-first companies now embrace hybrid team structures. While remote participation in meetings has become commonplace in this new environment, it may lead to isolation, alienation, and decreased engagement among remote team members. Aims. This study aims to iden- tify and characterize engagement patterns in hybrid meetings through objective measurements, focusing on the differences between co-located and remote participants. Method. We studied professionals from three software companies over several weeks, employing a multimodal approach to measure engagement. Data were collected through self-reported questionnaires and phys- iological measurements using biometric devices during hybrid meetings to understand engagement dynamics. Results. The re- gression analyses revealed comparable engagement levels between onsite and remote participants, though remote participants show lower engagement in long meetings regardless of participation mode. Active roles positively correlate with higher engagement, while larger meetings and afternoon sessions are associated with lower engagement. Conclusions. Our results offer insights into factors associated with engagement and disengagement in hybrid meetings, as well as potential meeting improvement recommenda- tions. These insights are potentially relevant not only for software teams but also for knowledge-intensive organizations across various sectors facing similar hybrid collaboration challenges
What do we know about software analytics research? : A critical review of secondary studies
No full textSoftware analytics (SA) is often proposed as a tool to support software engineering (SE) tasks. Several secondary studies on SA have been published, some published within the same calendar year. This presents an opportunity to take a meta-perspective and examine how the field of SA has been conceptualized and synthesized so far. By analyzing how SA is defined, which topics are emphasized, what search strategies are employed, and to what extent primary studies overlap, we aim to identify gaps, trends, and redundancies in the current body of secondary studies. Such insights can inform the design and focus of future secondary studies. We identified five secondary studies on SA published from 2015 to 2023 that cover primary research from 2000 to 2021. Despite similarities in objectives and overlapping search timeframes, the secondary studies have negligible overlap in their included primary studies. Each secondary study presents a distinct perspective, and collectively, the five secondary studies offer a fragmented rather than cohesive view of the research landscape. We present a structured overview of the identified secondary studies in terms of their objectives, research quality, and findings. This overview helps readers navigate and leverage existing research. The analysis also indicates that there is potential for further secondary research to build a more cohesive and comprehensive understanding of the SA literature
Lessons from Visualizing Software Architecture Structure Conformance at Thermo Fisher Scientific
No full textModern software-intensive systems are large and complex. Therefore, well-defined software architectures are required to manage such systems. However, implementing and maintaining a software architecture can be challenging in practice. For instance, architecture erosion can cause the implemented architecture to deviate from the intended one. Industrial practitioners often lack effective tools to check that an evolving software system continues to conform to its intended architecture. In this paper, we share the lessons we learned from adopting, extending, and applying a state-of-the-art architecture visualization tool for conformance checking the structure and dependencies between intended architecture (i.e. subsystems and their relations) and implemented architecture (i.e. implemented subsytems and their dependencies in the codebase) on a large industrial software project. Specifically, we collaborated with Thermo Fisher Scientific and used a graph-based visualization tool on one of the company’s systems. Using the tool, we can create a hierarchical view of layered software architectures including subsystem and component dependencies. During demonstration sessions, we presented the visualizations to 14 experts at Thermo Fisher Scientific who are involved with different subsystems to elicit their feedback. Using our tool, the experts found it easier to focus on relevant areas of the system and to detect architecture violations and anomalies. The experts expressed great enthusiasm for using the tool on their own. Our insights suggest that software architecture visualization tools can aid software architects in maintaining the conformance between intended and actual software architectures. However, applying existing tools faces challenges regarding scalability, usability, and the integration into company workflows. Our lessons highlight opportunities for future research and improvements in (open-source) software architecture tools.</p
What Happens, When Software Product Development Companies Go Global?
No full textGlobalization has significantly changed the way the market operates today. In particular, it motivated many software companies expand through acquisitions and utilize skillful resources regardless of their location around the globe. Global software engineering endeavors have been widely explored in the research literature for the past decade and associated with many challenges caused by geographic, temporal and cultural distances. While software development as such is quite a challenging task, involvement of dispersed and diverse software teams created a perceived crisis with respect to, so called, soft issues that have not been targeted in the past. The effect of distribution versus co-location is still under investigation, and the most commonly referred challenges are related to communication, coordination and control. Thus the focus of research to a large extent has shifted from software product as the center, to people developing software.</p
Symbolic Runtime Verification and Adaptive Decision-Making for Robot-Assisted Dressing
No full textWe present a control framework for robot-assisted dressing that augments low-level hazard response with runtime monitoring and formal verification. A parametric discrete-time Markov chain (pDTMC) models the dressing process, while Bayesian inference dynamically updates this pDTMC’s transition probabilities based on sensory and user feedback. Safety constraints from hazard analysis are expressed in probabilistic computation tree logic, and symbolically verified using a probabilistic model checker. We evaluate reachability, cost, and reward trade-offs for garment-snag mitigation and escalation, enabling real-time adaptation. Our approach provides a formal yet lightweight foundation for safety-aware, explainable robotic assistance
Socio-Technical Well-Being of Quantum Software Communities: An Overview on Community Smells
No full textQuantum computing has gained significant attention due to its potential to solve computational problems beyond the capabilities of classical computers. With major corporations and academic institutions investing in quantum hardware and software, there has been a rise in the development of quantum-enabled systems, particularly within open-source communities. However, despite the promising nature of quantum technologies, these communities face critical socio-technical challenges, including the emergence of socio-technical anti-patterns known as community smells. These anti-patterns, prevalent in open-source environments, have the potential to negatively impact both product quality and community health by introducing technical debt and amplifying architectural and code smells. Despite the importance of these socio-technical factors, there remains a scarcity of research investigating their influence within quantum open-source communities. This work aims to address this gap by providing a first step in analyzing the socio-technical well-being of quantum communities through a cross-sectional study. By understanding the socio-technical dynamics at play, it is expected that foundational knowledge can be established to mitigate the risks associated with community smells and ensure the long-term sustainability of open-source quantum initiatives.</p
Refactoring Detection Across Languages:Leveraging Java-Trained Models for Detecting Class-Level Refactorings in Kotlin
No full textAutomating refactoring detection is an important goal in software engineering, with the potential to significantly reduce development time and costs. However, the complexity of refactoring tasks and the need for language-specific knowledge make the development of general-purpose solutions challenging. While machine learning models have proven effective in detecting refactorings in widely used languages like Java, their applicability to other languages–such as Kotlin–remains largely unexplored, especially due to the limited availability of labeled datasets.This study investigates the feasibility of zero-shot cross-language refactoring detection by applying machine learning models trained on Java to identify refactorings in Kotlin. We employ Random Forest classifiers, originally trained on Java code, to detect seven types of class-level refactorings in Kotlin projects. For evaluation, we use RefDetect–an established refactoring detection tool–to extract 2,540 class-level refactorings from approximately 30,000 commits across 10 open-source Kotlin repositories. The Java-trained models are then applied to these projects, and their predictions are compared against RefDetect’s results to assess accuracy and generalizability.Our evaluation shows that the Java-trained models achieve an average F-score of 71% in detecting class-level refactorings in Kotlin. The model demonstrates strong recall (84%), indicating its ability to detect most actual refactorings, but precision is more modest (66%), suggesting a higher rate of false positives. Our analysis shows that model performance is influenced by both the type of refactoring and the characteristics of individual repositories. Refactorings such as Move Class, Extract Interface, and Rename Class consistently yield high F-scores–often above 90%. In contrast, less frequent refactorings like Extract Subclass and Extract Superclass yield significantly lower F-scores, around 40%. Detection accuracy for individual refactoring types also differs between repositories, implying that project-specific factors (e.g., code structure and refactoring frequency) influence model predictions. This observed variability highlights the potential benefits of tailoring detection strategies to match the unique characteristics of each project and refactoring type
Fair comparison of developing software in different locations dynamic decision model
No full textWhile offshore software development is frequently looked at in simple economic terms, assumed benefits are not always achieved. This can be attributed to unexpectedly high transition costs and a number of extra costs for maintaining a sourcing strategy. When offshoring fails, companies need to revisit their decision. In this paper, an analytical model is proposed to support evaluation of offshoring strategies and decision options. The model focuses on value comparison, and treats outcomes of offshoring relationships more fairly and realistically than a naïve salary comparison. The model is a development of models with extensions motivated by lessons learned from two empirical cases. The model is designed to consider more decision outcomes than previously suggested. In contrast to current practice focusing on economic profits from the salary differences between onshore and offshore, we focus on the differences between expected and realised outcomes by analysing strategic benefits created by options and measuring the same time-value. Finally, consequent decision situations provide a dynamic view on supporting chains of decisions
Risk Identification in Software Product Transfers
No full textSoftware companies today often face the necessity to decide where to develop
their products. Inability to employ people or continue ongoing development with
the same capacity in a given site of a company often leads to relocation of
software work from one site to another. Software product transfers, however,
are associated with numerous challenges that require investments, and may also
have a secondary harder to capture effect on development productivity, quality
and scope. In this paper, we share the results from previous empirical studies
of software product transfers and offer a checklist for risk identification.
The checklist shall be useful for software companies that consider, plan or
execute software transfers. Although many risk factors included in the
checklist may seem obvious, our empirical observations indicate that prior to
obtaining the necessary experience these factors have been initially
overlooked. Thus we believe that the checklist will be especially useful for
managers with no or little experiences in relocating software work between the
two sites of the same company
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