Scientific exploration is a relay race across generations. From Darwin's observations on plant movement and adaptation, to Mendel's elucidation of fundamental genetic principles using peas, to the modern molecular dissection of plant stress response mechanisms, every advance has depended not only on the strategic vision of established scientists but on their mentorship of young scholars and graduate students in the laboratory as well. It is this intergenerational transmission that enables each generation of researchers to revisit problems and questions with new tools, building on prior knowledge and continually pushing the boundaries of inquiry. Within this chain of succession, one group is critically important yet often overlooked. This group is Early Career Researchers, or ECRs. As defined for this Special Issue, ECRs are investigators who have obtained their PhD within the last 7 years and are under 40 years of age. They are the backbone of daily laboratory operations, deeply involved in experimental design, data acquisition, and technical problem solving. They are adept at emerging technologies and possess a practical understanding of the strengths and limitations of modern research tools. Although ECRs perform the majority of the research execution in most teams, their opportunities to publish as corresponding authors are often limited, and their independent academic identity is frequently intertwined with the guidance of senior mentors. This situation constrains their own career development to some extent and, at the same time, hinders the field from fully benefiting from diverse perspectives and fresh ideas, precisely the kind of vitality that sustains disciplinary progress. Recognizing the importance of identifying and supporting early career talent, several plant science journals under Wiley, including JIPB, Nordic Journal of Botany, Food and Energy Security, Plant Environment Interactions, and our own Plant, Cell and Environment, jointly launched this Special Issue on Early Career Researchers in Focus, Breakthroughs in Plant Science in August 2024. The response was overwhelming, reflecting the remarkable creativity of researchers at this career stage. Over the past 2 years, PCE received over 100 submissions. Following rigorous peer review, 28 papers were ultimately accepted for publication. This Special Issue serves both as a showcase of ECRs' scientific contributions and as a concrete effort to build an academic platform for them. The breadth of research covered in this issue is truly impressive, reflecting the multifaceted nature of modern plant science, ranging from macroecology to molecular mechanisms. Thematically, the papers encompass everything from global-scale ecological surveys to local physiological adaptation mechanisms. Methodologically, they extend from traditional physiological measurements to cutting-edge deep learning applications and CRISPR gene editing technologies. The issue comprises 23 original research articles, 2 brief communications and 3 review articles, covering topics such as volatile-mediated plant defense (Li et al. 2026), the molecular regulation of anthocyanin biosynthesis in apple under low nitrogen (Meng et al. 2026), and transgene-free genome editing in trees using CRISPR ribonucleoproteins (Ramakrishnan et al. 2025). Several contributions integrate or synthesize multi-omics evidence to reveal novel molecular mechanisms, including the role of lysine acetylation in plant immunity (Villette et al. 2025) and lipidome reprogramming that enhances photosynthetic performance (Deepa et al. 2026). These works are not merely incremental advances; they also propose new conceptual frameworks, for example by revealing a universal genome size–cell size relationship across land plants (Mir-Rosselló et al. 2025) and demonstrating the decoupling of water and nitrogen translocation from subsoil to the canopy in forest trees (Mrak et al. 2025). By bringing these outstanding contributions together in a single collection, this Special Issue serves both to highlight the remarkable contributions of ECRs and to inspire future discoveries. We are confident that these emerging leaders will continue to shape the trajectory of plant science, and we hope this issue serves as an entry point for readers to appreciate their exceptional research. Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
Some claim that especially in the field of agile software development the research lags years behind of the practice. In this paper, we characterize the status and main challenges for research on agile software development, and propose a preliminary roadmap, focusing on providing more empirical research, primarily on experienced agile teams and organizations, connecting better to existing streams of research in more established fields, giving more attention to management-oriented approaches, and finally give more emphasis to the core ideas in agile software development in order to increase our understanding. We hope that this preliminary roadmap serves as a starting point for creating a common research agenda and enables the generation of fruitful discussions and research results from the field.
Torgeir Dingsøyr, T. Dybå, P. Abrahamsson· Agile Conference· 91 citations· ⚡7
The happy-productive worker thesis states that happy workers are more productive. Recent research in software engineering supports the thesis, and the ideal of flourishing happiness among software developers is often expressed among industry practitioners. However, the literature suggests that a cost-effective way to foster happiness and productivity among workers could be to limit unhappiness. Psychological disorders such as job burnout and anxiety could also be reduced by limiting the negative experiences of software developers. Simultaneously, a baseline assessment of (un)happiness and knowledge about how developers experience it are missing. In this paper, we broaden the understanding of unhappiness among software developers in terms of (1) the software developer population distribution of (un)happiness, and (2) the causes of unhappiness while developing software. We conducted a large-scale quantitative and qualitative survey, incorporating a psychometrically validated instrument for measuring (un)happiness, with 2 220 developers, yielding a rich and balanced sample of 1318 complete responses. Our results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains. We also identified 219 factors representing causes of unhappiness while developing software. Our results, which are available as open data, can act as guidelines for practitioners in management positions and developers in general for fostering happiness on the job. We suggest considering happiness in future studies of both human and technical aspects in software engineering.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
To compete in this age of disruption, large companies cannot rely on cost efficiency, lead time reduction and quality improvement. They are now looking for ways to innovate like startups. Meanwhile, the awareness and use of the Lean startup approach have grown rapidly amongst the software startup community in recent years. This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors. A multiple case study approach is followed in the investigation. Two software product innovation projects from two large companies are examined, using a conceptual framework that is based on the method-in-action framework and extended with the previously developed Lean-Internal Corporate Venture model. Seven face-to-face in-depth interviews of the employees with different roles are conducted. Within-case analysis and cross-case comparison are applied to draw the findings from the cases. A generic process flow summarises the common key processes of Lean internal startups. The findings suggest that an internal startup that is initiated management or employees faces different challenges. A list of enablers of applying Lean startup in large companies are identified, including top management support and cross-functional team. Both cases face different inhibitors due to the different process of inception, objective of the team and type of the product. Our contributions are threefold. First, this study is one of the first attempt to investigate the use of Lean startup approach in large companies empirically. Second, the study shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context. The third is a general process of Lean internal startup and the evidence of the enablers and inhibitors of implementing it, which are both theory-informed and empirically grounded.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
Agile methods continue to gain popularity. In particular, the Scrum method appears to be on the verge of becoming a de-facto standard in the industry, leading the so called Agile movement. While there are success stories and recommendations, there is little scientifically valid evidence of the challenges in the adoption of Agile methods in general, and Scrum in particular. Little, if anything, is empirically known about the application and adoption of Scrum in a multi-team and multi-project situation. The authors carried out an ethnographically informed longitudinal case study in industrial settings and closely followed how the Scrum method was adopted in a 20-person department, working in a simultaneous multi-project R&D environment. Altogether 10 challenges pertinent to the case of multi-team multi-project Scrum adoption were identified in the study. The authors contend that these results carry great relevance for other industrial teams. Future research avenues arising from the study are indicated.
A. Marchenko, P. Abrahamsson· Agile Conference· 59 citations· ⚡11
Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This Review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas, such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 54 citations