The recent advancements of Small Language Models (SLMs) have opened new possibilities for efficient code generation. SLMs offer lightweight and cost-effective alternatives to Large Language Models (LLMs), making them attractive for use in resource-constrained environments. However, empirical understanding of SLMs, particularly their capabilities, limitations, and performance trade-offs in code generation remains limited. This study presents a comprehensive empirical evaluation of 20 open-source SLMs ranging from 0.4B to 10B parameters on five diverse code-related benchmarks (HumanEval, MBPP, Mercury, HumanEvalPack, and CodeXGLUE). The models are assessed along three dimensions: i) functional correctness of generated code, ii) computational efficiency and iii) performance across multiple programming languages. The findings of this study reveal that several compact SLMs achieve competitive results while maintaining a balance between performance and efficiency, making them viable for deployment in resource-constrained environments. However, achieving further improvements in accuracy requires switching to larger models. These models generally outperform their smaller counterparts, but they require much more computational power. We observe that for 10% performance improvements, models can require nearly a 4x increase in VRAM consumption, highlighting a trade-off between effectiveness and scalability. Besides, the multilingual performance analysis reveals that SLMs tend to perform better in languages such as Python, Java, and PHP, while exhibiting relatively weaker performance in Go, C++, and Ruby. However, statistical analysis suggests these differences are not significant, indicating a generalizability of SLMs across programming languages. Based on the findings, this work provides insights into the design and selection of SLMs for real-world code generation tasks.

As general-purpose LLMs become widely accessible, tailoring their outputs to domain-specific tasks remains challenging. This paper investigates how custom, pattern-guided datasets and fine-tuning can specialize LLM behavior. We run a four-round open-vs-closed comparison: (1) evaluate commercially hosted models with an open-source dataset; (2) repeat with a synthetic, pattern-aligned variant; (3) replicate both rounds with open-source LLMs; and (4) demonstrate a real-world case by fine-tuning an open model to answer WordPress technical support queries using a finalized, pattern-constrained dataset. Across rounds, fine-tuned open-source models achieved performance comparable to closed-source baselines while offering greater controllability and deployment flexibility. We detail dataset design, the role of response patterns in producing consistent outputs, and practical trade-offs observed during fine-tuning and real-world use. The findings indicate that with well-crafted custom datasets, open-source LLMs can be reliably specialized for targeted applications without reliance on proprietary APIs.

The use of Large Language Models (LLMs) for autonomous code generation is gaining attention in emerging technologies. As LLM capabilities expand, they offer new possibilities such as code refactoring, security enhancements, and legacy application upgrades. Many outdated web applications pose security and reliability challenges, yet companies continue using them due to the complexity and cost of upgrades. To address this, we propose an LLM-based multi-agent system that autonomously upgrades legacy web applications to the latest versions. The system distributes tasks across multiple phases, updating all relevant files. To evaluate its effectiveness, we employed Zero-Shot Learning (ZSL) and One-Shot Learning (OSL) prompts, applying identical instructions in both cases. The evaluation involved updating view files and measuring the number and types of errors in the output. For complex tasks, we counted the successfully met requirements. The experiments compared the proposed system with standalone LLM execution, repeated multiple times to account for stochastic behavior. Results indicate that our system maintains context across tasks and agents, improving solution quality over the base model in some cases. This study provides a foundation for future model implementations in legacy code updates. Additionally, findings highlight LLMs' ability to update small outdated files with high precision, even with basic prompts. The source code is publicly available on GitHub: https://github.com/alasalm1/Multi-agent-pipeline.

The growing need for scalable, maintainable, and fast-deploying systems has made microservice architecture widely popular in software development. This paper presents a system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices. This system assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages. By focusing on the API-first methodology, this system ensures that microservices are designed with well-defined interfaces, promoting consistency and reliability across the development life-cycle. The integration of log analysis enables the LLM to detect and address issues efficiently, reducing the number of iterations required to produce functional and robust services. This process automates the generation of microservices and also simplifies the debugging and refinement phases, allowing developers to focus on higher-level design and integration tasks. This system has the potential to benefit software developers, architects, and organizations to speed up software development cycles and reducing manual effort. To assess the potential of the system, we conducted surveys with six industry practitioners. After surveying practitioners, the system demonstrated notable advantages in enhancing development speed, automating repetitive tasks, and simplifying the prototyping process. While experienced developers appreciated its efficiency for specific tasks, some expressed concerns about its limitations in handling advanced customizations and larger-scale projects. The code is publicly available at https://github.com/sirbh/code-gen.

Context: Generative AI (GenAI) is argued to transform software engineering (SE) in various ways, and GenAI tools show promise for various SE tasks. Software organizations across the globe are currently exploring the use of GenAI for SE. Objective: While numerous studies have recently been published on GenAI, few studies have looked at the adoption of these tools and their usage from an organizational point of view, focusing instead on individual users. Our objective is to understand how organizations adopt these tools and what their impacts are in industrial contexts, with a focus on the European perspective. Method: We conducted a multiple case study of seven European companies. We collected data through semi-structured interviews (n=15), as well as through longitudinal observation in one case company. All data were analyzed using thematic analysis. Results: We analyzed 28 transcripts, resulting in 456 quotations and 557 code occurrences split between 66 individual codes that were categorized under 6 high-level themes. We identified 25 types of tasks GenAI was currently being used for in our case organizations. We identified 12 benefits for GenAI in SE and 10 adoption and use challenges. Key adoption challenges for organizations include data privacy and legislative concerns, the emerging and fast-moving market of GenAI tools, difficulty of measuring the positive impact of the tools, and potential change resistance. For individuals, the key challenges are related to prompting, such as understanding what a good prompt is, and how to write prompts for specific tasks. Conclusion: GenAI adoption is becoming widespread in SE, but good practices and use cases are still emerging. While GenAI can potentially produce various benefits in SE, companies and individual users are facing various challenges in making the most of GenAI in SE. Our results provide insights into the current state of practice.

ABSTRACT Context Generative artificial intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAI's ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Objective Although many recent publications have explored and evaluated the application of GenAI, a comprehensive understanding of the current development, applications, limitations, and open challenges remains unclear to many. Particularly, we do not have an overall picture of the current state of GenAI technology in practical software engineering usage scenarios. Method We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for software engineering. Results We identified 78 open research questions (RQs) in 11 areas of software engineering. Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision‐making in all software development activities. While the current literature is skewed toward software implementation, quality assurance and software maintenance, other areas, such as requirements engineering, software design, and software engineering education, would need further research attention. Common considerations when implementing GenAI include industry‐level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology. Conclusions GenAI is bringing significant changes to the field of software engineering. Nevertheless, the state of research on the topic still remains immature. We believe that this research agenda holds significance and practical value for informing both researchers and practitioners about current applications and guiding future research.

Generative artificial intelligence has attracted global attention and interest in software engineering research, practical applications, and in business, especially in the past two years. Tools like Gemini, Copilot, and ChatGPT have been widely studied in various professional contexts for their exceptional ability to produce human-like content. Furthermore, the number of these artificial intelligence tools is increasing explosively. Within this context we have picked a very specific use case: namely, converting notes into a full, uniform report using ChatGPT's custom instructions. In this paper, we present the process of creating custom instructions for a certain task, how the instructions were tested, and the results of our study. The study shows that even though using custom instructions alone is perhaps not quite sufficient yet for fully automatic report generation, the approach can still save a considerable amount of time and resources. This study also highlights the pitfalls and obstacles faced, and should help those who are planning to embark on a similar quest.

Abstract In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the application of large language models (LLMs) to improve the quality of user stories within the agile teams of Austrian Post Group IT. We developed an Autonomous LLM-based Agent System (ALAS) and evaluated its impact on user story quality with 11 participants from six agile teams. Our findings reveal the potential of LLMs in improving user story quality, provide a practical example, and lay the foundation for future research into the broad application of LLMs in a variety of industry settings.

Context The COVID‐19 pandemic has had a disruptive impact on how people work and collaborate across all global economic sectors, including software business. While remote working is not new for software engineers, forced WFH situations come with both limitations and opportunities. As the ‘new normal’ for working might be based on the current state of Work‐from‐home (WFH), it is useful to understand what has happened and learn from that. Objective This study aims to gain insights into how their WFH arrangement impacts project management and software engineering. We are also interested in exploring these impacts in different contexts, such as startups and established companies. Method We conducted a global‐scale, cross‐sectional survey during the spring and summer 2021. Our results are based on quantitative and qualitative analysis of 297 valid responses. Results We characterize the profile of WFH in both spatial and temporal aspects, together with a set of common collaborative tools and coordination and control mechanisms. We revealed some areas of project management that are relatively more challenging during WFH situations, such as coordination, communication and project planning. We also revealed a mixed picture of the perceived impact of WFH on different software engineering activities. Conclusion WFH is a situational phenomenon which can have both negative and positive impact on software teams. For practitioners, we suggest a unified approach to consider the context of WFH, collaborative tools, associated coordination and control approaches and a process that resolve those aspects that are sensitive to physical interaction.

Society’s increasing dependence on Artificial Intelligence (AI) and AI-enabled systems require a more practical approach from software engineering (SE) executives in middle and higher-level management to improve their involvement in implementing AI ethics by making ethical requirements part of their management practices. However, research indicates that most work on implementing ethical requirements in SE management primarily focuses on technical development, with scarce findings for middle and higher-level management. We investigate this by interviewing ten Finnish SE executives in middle and higher-level management to examine how they consider and implement ethical requirements. We use ethical requirements from the European Union (EU) Trustworthy Ethics guidelines for Trustworthy AI as our reference for ethical requirements and an Agile portfolio management framework to analyze implementation. Our findings reveal a general consideration of privacy and data governance ethical requirements as legal requirements with no other consideration for ethical requirements identified. The findings also show practicable consideration of ethical requirements as technical robustness and safety for implementation as risk requirements and societal and environmental well-being for implementation as sustainability requirements. We examine a practical approach to implementing ethical requirements using the ethical risk requirements stack employing the Agile portfolio management framework.

Non peer reviewed

Software startups are important drivers of economy on a global scale, and have become associated with innovation and high growth. However, the overwhelming majority of startups ends in failure. Many of these startup failures ultimately stem from software engineering issues, and requirements engineering (RE) ones in particular. Despite the emphasis placed on the importance of RE activities in the startup context, many startups continue to develop software without a clear market or customer, having never had meaningful contact with their would-be customer. We develop a method aimed at early-stage startups that is intended to help startups through the initial stages of the startup process: StartCards. The method emphasizes the importance of idea and product validation activities in particular in order to tackle anti-patterns related to (a lack of) RE in startups. This method is based on existing literature, both grey and academic literature. StartCards was developed using the Canonical Action Research (CAR) approach, over the course of 4 AR cycles. During the AR process, the method was used by 44 student startup teams in a practical course setting. Data from the use of the method was collected through self-reporting in the form of modified learning diaries, mentoring meetings with the startup teams, and a qualitative survey. We consider the current version of StartCards useful for early-stage startups based on the data we have collected. The method can also be used as a pedagogical tool in startup education. The paper presents the first published version of the method. While work on the method continues, the method is deemed ready for use.

Governance is crucial in achieving the success and sustainability of blockchain systems. However, blockchain governance is multi-faceted, complex, dynamic, and challenging due to its decentralized nature and automatically enforced rules and mechanisms. This study aims to advance the theory of blockchain governance and support practitioners to deepen the researchers’ and practitioners’ understanding of blockchain governance. The study is a systematic literature review of 75 articles that applies systems theory to conceptualize blockchain governance as a system and parsimoniously organize its interrelated elements into a conceptual model. The paper proposes a holistic definition and a conceptual model of blockchain governance. Blockchain governance encompasses technical and social means to make decisions on the different levels (e.g., individual, community, organizational, national, international) related to actors, roles, rights, incentives, responsibilities, rules, and the business, technological, legal, and regulatory aspects of a blockchain system during its whole lifecycle. The system-based model of blockchain governance can serve as a reference framework and structured foundation for analyzing, discussing, and developing the governance of blockchain systems.

Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture-centric processes, practices, description languages to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted a Systematic Literature Review (SLR) to investigate (i) architectural process, (ii) modelling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems; however, existing processes and notations can be tailored to derive the architecting activities and develop modelling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customise the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software.

Artificial intelligence (AI) solutions and technologies are being increasingly adopted in smart systems contexts; however, such technologies are concerned with ethical uncertainties. Various guidelines, principles, and regulatory frameworks are designed to ensure that AI technologies adhere to ethical well-being. However, the implications of AI ethics principles and guidelines are still being debated. To further explore the significance of AI ethics principles and relevant challenges, we conducted a survey of 99 randomly selected representative AI practitioners and lawmakers (e.g., AI engineers and lawyers) from 20 countries across five continents. To the best of our knowledge, this is the first empirical study that unveils the perceptions of two different types of population (AI practitioners and lawmakers) and the study findings confirm that transparency, accountability, and privacy are the most critical AI ethics principles. On the other hand, lack of ethical knowledge, no legal frameworks, and lacking monitoring bodies are found to be the most common AI ethics challenges. The impact analysis of the challenges across principles reveals that conflict in practice is a highly severe challenge. Moreover, the perceptions of practitioners and lawmakers are statistically correlated with significant differences for particular principles (e.g. fairness and freedom) and challenges (e.g. lacking monitoring bodies and machine distortion). Our findings stimulate further research, particularly empowering existing capability maturity models to support ethics-aware AI systems’ development and quality assessment.

Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response to the challenges related to AI. Transparency poses a key challenge for implementing AI ethics in practice. One solution to transparency issues is AI systems that can explain their decisions. Explainable AI (XAI) refers to AI systems that are interpretable or understandable to humans. The research fields of AI ethics and XAI lack a common framework and conceptualization. There is no clarity of the field’s depth and versatility. A systematic approach to understanding the corpus is needed. A systematic review offers an opportunity to detect research gaps and focus points. This article presents the results of a systematic mapping study (SMS) of the research field of the Ethics of AI. The focus is on understanding the role of XAI and how the topic has been studied empirically. An SMS is a tool for performing a repeatable and continuable literature search. This article contributes to the research field with a Systematic Map that visualizes what, how, when, and why XAI has been studied empirically in the field of AI ethics. The mapping reveals research gaps in the area. Empirical contributions are drawn from the analysis. The contributions are reflected on in regards to theoretical and practical implications. As the scope of the SMS is a broader research area of AI ethics, the collected dataset opens possibilities to continue the mapping process in other directions.

The public and academic discussion on Artificial Intelligence (AI) ethics is accelerating and the general public is becoming more aware AI ethics issues such as data privacy in these systems. To guide ethical development of AI systems, governmental and institutional actors, as well as companies, have drafted various guidelines for ethical AI. Though these guidelines are becoming increasingly common, they have been criticized for a lack of impact on industrial practice. There seems to be a gap between research and practice in the area, though its exact nature remains unknown. In this paper, we present a gap analysis of the current state of the art by comparing practices of 39 companies that work with AI systems to the seven key requirements for trustworthy AI presented in the “The Ethics Guidelines for Trustworthy Artificial Intelligence”. The key finding of this paper is that there is indeed notable gap between AI ethics guidelines and practice. Especially practices considering the novel requirements for software development, requirements of societal and environmental well-being and diversity, nondiscrimination and fairness were not tackled by companies.

Abstract Artificial Intelligence (AI) systems are increasing in significance within software services. Unfortunately, these systems are not flawless. Their faults, failures and other systemic issues have emphasized the urgency for consideration of ethical standards and practices in AI engineering. Despite the growing number of studies in AI ethics, comparatively little attention has been placed on how ethical issues can be mitigated in software engineering (SE) practice. Currently understanding is lacking regarding the provision of useful tools that can help companies transform high-level ethical guidelines for AI ethics into the actual workflow of developers. In this paper, we explore the idea of using user stories to transform abstract ethical requirements into tangible outcomes in Agile software development. We tested this idea by studying master’s level student projects (15 teams) developing web applications for a real industrial client over the course of five iterations. These projects resulted in 250+ user stories that were analyzed for the purposes of this paper. The teams were divided into two groups: half of the teams worked using the ECCOLA method for AI ethics in SE, while the other half, a control group, was used to compare the effectiveness of ECCOLA. Both teams were tasked with writing user stories to formulate customer needs into system requirements. Based on the data, we discuss the effectiveness of ECCOLA, and Primary Empirical Contributions (PECs) from formulating ethical user stories in Agile development.

Artificial Intelligence (AI) systems are becoming increasingly widespread and exert a growing influence on society at large. The growing impact of these systems has also highlighted potential issues that may arise from their utilization, such as data privacy issues, resulting in calls for ethical AI systems. Yet, how to develop ethical AI systems remains an important question in the area. How should the principles and values be converted into requirements for these systems, and what should developers and the organizations developing these systems do? To further bridge this gap in the area, in this paper, we present a method for implementing AI ethics: ECCOLA. Following a cyclical action research approach, ECCOLA has been iteratively developed over the course of multiple years, in collaboration with both researchers and practitioners.

There is a rapidly increasing amount of Artificial Intelligence (AI) systems developed in recent years, with much expectation on its capacity of innovation and business value generation. However, the promised value of AI systems in specific business contexts might not be understood, and further integrated into the development processes. We wanted to understand how software engineering processes and practices can be applied to develop AI systems in a fast-faced, business-driven manner. As the first step, we explored contextual factors of AI development and the connections between AI developments to business opportunities. We conducted 12 semi-structured interviews in seven companies in Brazil, Norway and Southeast Asia. Our investigation revealed different types of AI systems and different AI development approaches. However, it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development of AI systems. The findings have implications for future research on business-driven AI development and supporting tools and practices.

Business Model Canvas (BMC) is a tool widely used to describe startup business models. Despite the various business aspects described, BMC pays a little emphasis on team- related factors. The importance of team-related factors in software development has been acknowledged widely in literature. While not as extensively studied, the importance of teams in software startups is also known in both literature and among practitioners. In this paper, we propose potential changes to BMC to have the tool better reflect the importance of the team, especially in a software startup environment. Based on a literature review, we identify various components related to the team, which we then further support with empirical data. We do so by means of a qualitative case study of five startups.

Startups are often seen as drivers of innovation. In an attempt to leverage this potential, larger business organizations have founded internal startups as a subset of internal corporate ventures (ICV). These smaller organizations are intended to be more agile than the parent organization, in order to produce new service and product innovations using their own methods and practices independently of the organizational culture and methods of the parent organization. However, our understanding of ICVs is still lacking in terms of processes and success factors, and especially the more recent internal startups have scarcely been studied thus far. To approach this novel area of research, we take on a qualitative approach by means of a multiple case study of internal startups in large companies. Based on the data, we argue that the origin of the idea of the internal startup heavily influences the processes utilized by the internal startup, as well as the connections between the internal startup and its parent organization. We also highlight various practical implications.

Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Moving from experiments to industrial level AI software development requires a shift from understanding AI/ ML model attributes as a standalone experiment to know-how integrating and operating AI models in a large-scale software system. It is a growing demand for adopting state-of-the-art software engineering paradigms into AI development, so that the development efforts can be aligned with business strategies in a lean and fast-paced manner. We describe AI development as an “unknown unknown” problem where both business needs and AI models evolve over time. We describe a holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products. From this, five areas of challenges are presented with the focus on experimentation. In the end, we propose a research agenda with seven questions for future studies.

Artificial Intelligence (AI) solutions are becoming increasingly common in software development endeavors, and consequently exert a growing societal influence as well. Due to their unique nature, AI based systems influence a wide range of stakeholders with or without their consent, and thus the development of these systems necessitates a higher degree of ethical consideration than is currently carried out in most cases. Various practical examples of AI failures have also highlighted this need. However, there is only limited research on methods and tools for implementing AI ethics in software development, and we currently have little knowledge of the state of practice. In this study, we explore the state of the art in startup-like environments where majority of the AI software today gets developed. Based on a multiple case study, we discuss the current state of practice and highlight issues. The cases underline the complete ignorance of ethical consideration in AI endeavors. We also outline existing good practices that can already support the implementation of AI ethics, such as documentation and error handling.

Software Engineers work using highly diverse methods and practices, and general theories in software engineering are lacking. A recent attempt at creating a common ground in the area of software engineering methodologies has been the Essence Theory of Software Engineering. Essence is a method-agnostic progress management framework and a meta-method for Software Engineering (SE). However, tooling for Essence is still lacking. Without dedicated tools and other instruments, a meta-method such as Essence is cumbersome to utilize by practitioners and students. Indeed, Essence currently suffers from a lack of widespread practitioner adoption. In this paper, we thus present an Open Source tool for essentializing methods and practices: Essencery. We conduct a qualitative evaluation of the tool through a quasi-formal experiment and a set of semi-structured interviews. Based on this data, we improve Essencery iteratively before it is utilized in a large-scale project-based course as a proof of concept.

Cyber Physical Systems (CPS) development approaches tend to start from the physical (hardware) perspective, and the software is the final element in the process. However, this approach is unfit for the more software-intensive world that is increasingly iterative, connected, and constantly online. Many constraints prevent the application of iterative, incremental, and agile development methodologies, which now are the norm for many other fields of software. Time-consuming system validation can only start when both hardware and software components are ready, which implies that the software delivery and quality is almost always the final bottleneck in the CPS development and integration. Also organizational issues raise concerns - CPS development teams are nowadays often geographically distributed, which can result in delays in the process, shortcomings, and even mistakes. In this paper, we propose using our envisioned open-source Virtual Reality-based Integrated software Development Environment (VRIDE) for developing the next generation, increasingly software-intensive CPSs in efficient ways.

Use of artificial intelligence (AI) in human contexts calls for ethical considerations for the design and development of AI-based systems. However, little knowledge currently exists on how to provide useful and tangible tools that could help software developers and designers implement ethical considerations into practice. In this paper, we empirically evaluate a method that enables ethically aligned design in a decision-making process. Though this method, titled the RESOLVEDD strategy, originates from the field of business ethics, it is being applied in other fields as well. We tested the RESOLVEDD strategy in a multiple case study of five student projects where the use of ethical tools was given as one of the design requirements. A key finding from the study indicates that simply the presence of an ethical tool has an effect on ethical consideration, creating more responsibility even in instances where the use of the tool is not intrinsically motivated.

Internet-of-Things applications are not only the new opportunity for digital businesses but also a major driving force for the modification and creation of software systems in all industries and businesses. Compared to other types of software-intensive products, the development of Internet-of-Things applications lacks a systematic approach and guidelines. This paper aims at understanding the methodological commonalities among startups who are developing Internet-of-Things products. Using the SEMAT Essence framework, we captured common team compositions, common types of Minimum Viable Products and common way of working in early stage Internet-of-Things startups. We found that startups include various engineering and business competence, but do not cover all of what is needed. The development of Internet-of-Things applications adopts certain speed-favor approaches, i.e. rapid prototyping, iterative development and outsourcing. The finding implies some recommendations for both researchers and practitioners in the area of Internet-of-Things development.

[Context] Advancement in technologies, popularity of small-batch manufacturing and the recent trend of investing in hardware startups are among the factors leading to the rise of hardware startups nowadays. It is essential for hardware startups, companies that involve both software and hardware development, to be not only agile to develop their business but also efficient to develop the right products. [Objective] We investigate how hardware startups achieve agility when developing their products in early stages. [Methods] A qualitative research is conducted with data from 20 hardware startups. [Result] Preliminary results show that agile development is known to hardware entrepreneurs, however it is limitedly adopted. We also found four categories of tactics: (1) strategy, (2) personnel, (3) artifact and (4) resource that enable hardware startups agile in their early stage business and product development. [Conclusions] Agile methodologies should be adopted with the consideration of specific features of hardware development, such as up-front design and vendor dependencies.

This extended abstract summarises an article, which has been published in the Information and Software Technology (IST) Journal and was selected for the Journal-First presentations at the International Conference on Software and System Process (ICSSP 2018). Full Article Reference. Henry Edison, Xiaofeng Wang, Ronald Jabangwe, and Pekka Abrahamsson. 2018. Innovation Initiatives in Large Software Companies: A Systematic Mapping Study. Information and Software Technology, 95:1--14, (March 2018) DOI:10.1016/j.infsof.2017.12.007.

To keep the competitive advantage and adapt to changes in the market and technology, companies need to innovate in an organised, purposeful and systematic manner. However, due to their size and complexity, large companies tend to focus on maintaining their business, which can potentially lower their agility to innovate. This study aims to provide an overview of the current research on innovation initiatives and to identify the challenges of implementing the initiatives in the context of large software companies. The investigation was performed using a systematic mapping approach of published literature on corporate innovation and entrepreneurship. Then it was complemented with interviews with four experts with rich industry experience. Our study results suggest that, there is a lack of high quality empirical studies on innovation initiative in the context of large software companies. A total of 7 studies are conducted in such context, which reported 5 types of initiatives: intrapreneurship, bootlegging, internal venture, spin-off and crowdsourcing. Our study offers three contributions. First, this paper represents the map of existing literature on innovation initiatives inside large companies. The second contribution is to provide an innovation initiative tree. The third contribution is to identify key challenges faced by each initiative in large software companies. At the strategic and tactical levels, there is no difference between large software companies and other companies. At the operational level, large software companies are highly influenced by the advancement of Internet technology. Large software companies use open innovation paradigm as part of their innovation initiatives. We envision a future work is to further empirically evaluate the innovation initiative tree in large software companies, which involves more practitioners from different companies.

The growing literature on affect among software developers mostly reports on the linkage between happiness, software quality, and developer productivity. Understanding happiness and unhappiness in all its components – positive and negative emotions and moods – is an attractive and important endeavor. Scholars in industrial and organizational psychology have suggested that understanding happiness and unhappiness could lead to cost-effective ways of enhancing working conditions, job performance, and to limiting the occurrence of psychological disorders. Our comprehension of the consequences of (un)happiness among developers is still too shallow, being mainly expressed in terms of development productivity and software quality. In this paper, we study what happens when developers are happy and unhappy while developing software. Qualitative data analysis of responses given by 317 questionnaire participants identified 42 consequences of unhappiness and 32 of happiness. We found consequences of happiness and unhappiness that are beneficial and detrimental for developers’ mental well-being, the software development process, and the produced artifacts. Our classification scheme, available as open data enables new happiness research opportunities of cause-effect type, and it can act as a guideline for practitioners for identifying damaging effects of unhappiness and for fostering happiness on the job.

Decision-making in an emergency department needs to be efficient. It does not allow observation of the patient for a prolonged period of time, especially if the patients harm themselves or others, or refuses treatment. This includes suicidal, violent, intentional self-inflicted or non-consenting to treatments patient. Clinicians have to quickly decide whether to call the police, admit the patient to the psychiatric ward, according to recommended, predefined procedures. Since, for different situations different laws are applicable, clinicians need to follow certain procedures and consider specific legal issues while also handling challenging patients at the emergency department. Finding the right information quickly and following the recommended procedure is essential since the repercussions for a doctor acting incorrectly could be severe. This paper presents a mobile decision support system (DSS) prototype designed for healthcare providers to identify and follow the correct procedure based on applicable laws to mitigate the chance of incorrect actions being taken, along with the associated risks and preserve both the patients and their own rights. We explored healthcare providers requirements and alternatives for visualizing information by conducting observation, semi structured interviews and a total of four co-design workshops. The resulting mobile DSS enables clinicians to quickly access the correct information regarding their power and authority while also protecting the safety and welfare of the patient. Usability evaluation of the prototype showed that the average time spent to complete a given task was about 12 seconds by a total of six medical interns. Participants of the evaluation described the system easy to use, intuitive and highly useful for everyday use.

Abstract -- Software startups are becoming increasingly popular in software industry as well as other sectors of economy. Startups that lack necessary competences often seek for external resources from outsourcing partners. Little is known how this outsourcing relationship works and whether it makes sense to outsource the technical competence to an external party. This is among the first investigations on the outsourcing relationships in software startups. By conducting exploratory case studies at six startups, we found a mixed experience with outsourcing. The experimental nature of an early product development makes outsourcing a feasible option, although startups often suffer from its uncertainty and managing commitments from partners. Results further propose that early contract-based activities could be transformed into a long-term partnership by adopting a startup boundary spanner's role, establishing an inter-personal relationship and maintaining a mutual commitment.1

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.

It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds; firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startup’s progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototyping.

Software startups operate under various uncertainties and the demand on their ability to deal with change is high. Agile methods are considered a suitable and viable development approach for them. However, the competing needs for speed and quality may render certain agile practices less suitable than others in the startup context. The adoption of agile practices can be further complicated in software startups that adopt the Lean Startup approach. To make the best of agile practices, it is necessary to first understand whether and how they are used in software startups. This study targets at a better understanding of the use of agile practices in software startups, with a particular focus on lean startups. Based on a large survey of 1526 software startups, we examined the use of five agile practices, including quality related (regular refactoring and test first), speed related (frequent release and agile planning) and communication practice (daily standup meeting). The findings show that speed related agile practices are used to a greater extent in comparison to quality practices. Daily standup meeting is least used. Software startups who adopt the Lean Startup approach do not sacrifice quality for speed more than other startups do.

Editorial article Since the publication of Agile Manifesto in 2001, agile methods have transited from a grass- root initiative among enthusiastic advocates and developers to a mainstream software development approach adopted by both small and large companies worldwide. Meanwhile research on agile methods has grown rapidly and steadily into an established research area, evidenced by dedicated conferences (e.g., XP conference series, research track of previous Agile Conference series), special issues and sections in top Information Systems and Software Engineering journals. However, practitioners and consultants have largely driven the advancement in agile field, and agile research has lagged behind practice in the past. Has this situation changed as both agile methods and research community become increasingly mature? To be able to answer such questions, there is a constant need to check what interest agile practitioners and what agile researchers are investigating, to make sure that the states of the art and practice are aligned properly.

Product innovation is a risky activity, but when successful, it enables large software companies accrue high profits and leapfrog the competition. Internal startups have been promoted as one way to foster product innovation in large companies, which allows them to innovate as startups do. However, internal startups in large companies are challenging endeavours despite of the promised benefits. How large software companies can leverage internal startups in software product innovation is not fully understood due to the scarcity of the relevant studies. Based on a conceptual framework that combines the elements from the Lean startup approach and an internal corporate venturing model, we conducted a case study of a large software company to examine how a new product was developed through the internal startup effort and struggled to achieve the desired outcomes set by the management. As a result, the conceptual framework was further developed into a Lean startup-enabled new product development model for large software companies.

Startup process is a series of experiments before reaching a successful product. However, it is often difficult for startup founders to learn from their own experiences as well as from other startups. In order to capture the evolution of product and customer, we developed a theoretical model for early stage software startups. The model was adopted to describe five European software startups. Data was collected by interviews and documents. The model revealed the co-evolution patterns of product and customer knowledge. In addition, it discovers a common startup patterns among our cases, such as prototype-centric development and scale-up Agile. Last but not least, the model highlights signification pivots and possible relationship among them.

Minimum viable product (MVP) is the main focus of both business and product development activities in software startups. We empirically explored five early stage software startups to understand how MVP are used in early stages. Data was collected from interviews, observation and documents. We looked at the MVP usage from two angles, software prototyping and boundary spanning theory. We found that roles of MVPs in startups were not fully aware by entrepreneurs. Besides supporting validated learning, MVPs are used to facilitate product design, to bridge communication gaps and to facilitate cost-effective product development activities. Entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP). The work also implies several research directions about prototyping practices and patterns in software startups.

Software startups are challenging endeavours, with various road blocks on their path to success. The current understanding of the challenges that software startups may encounter is very limited. In this paper, we use the research framework of learning and product development stages to analyse the key challenges that software startups have to deal with at different life cycle stages, from problem definition to solution validation and from concept to mature product. Based on an analysis of the empirical data collected by a large survey of 4100 startups, we find out that what perceived as biggest challenges by software startups do vary across different life cycle stages. Building product is the biggest obstacle for software startups, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature. Business related challenges such as customer acquisition and scaling are more noticeable at the later stages. Our study raises the awareness of these challenges and suggests to tackle right challenges at the right time.

Software startups are newly created companies with no operating history and oriented towards producing cutting-edge products. However, despite the increasing importance of startups in the economy, few scientific studies attempt to address software engineering issues, especially for early-stage startups. If anything, startups need engineering practices of the same level or better than those of larger companies, as their time and resources are more scarce, and one failed project can put them out of business. In this study we aim to improve understanding of the software development strategies employed by startups. We performed this state-of-practice investigation using a grounded theory approach. We packaged the results in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible. This strategy allows startups to verify product and market fit, and to adjust the product trajectory according to early collected user feedback. The need to shorten time-to-market, by speeding up the development through low-precision engineering activities, is counterbalanced by the need to restructure the product before targeting further growth. The resulting implications of the GSM outline challenges and gaps, pointing out opportunities for future research to develop and validate engineering practices in the startup context.

Software startups are increasingly important in generating innovative products and services that widely impact global economy. However, there is an insufficient research support for their innovation and engineering activities, from both theoretical and empirical perspectives. This study aims at proposing a conceptual model to understand how these activities occur and evolve in software startups. The model is constructed using a complexity theory and system design thinking. The model usage is illustrated by describing three software startup stories. The results show that our model can capture innovation generation activities and the non-linear evolution of engineering activities in software startups, which is not presentable in current software life cycle models.

Abstract Software engineering (SE) research lacks theory and methodologies for addressing human aspects in software development. Development tasks are undertaken through cognitive processing activities. Affects (emotions, moods, and feelings) have a linkage to cognitive processing activities and the productivity of individuals. SE research needs to incorporate affect measurements to valorize human factors and to enhance management styles. The objective of this study is to analyze the affects dimensions of valence, arousal, and dominance of software developers and their real‐time correlation with their self‐assessed productivity (sPR). A repeated measurements design was conducted with eight participants (four students and four professionals), conveniently sampled and studied individually over 90 min of programming. The analysis was performed by fitting a linear mixed‐effects (LME) model. The results showed that valence and dominance are positively correlated with the sPR. The model was able to express about 38% of deviance from the sPR. Many lessons were learned when employing psychological measurements in SE and for fitting LME. This article demonstrates the value of applying psychological tests in SE and echoes a call to valorize the human individualized aspects of software developers. It reports a body of knowledge about affects, their classification, their measurement, and the best practices to perform psychological measurements in SE with LME models. Copyright © 2014 John Wiley & Sons, Ltd.

Context: Companies increasingly strive to adapt to market and ecosystem changes in real time. Evaluating team performance in such changing environments presents a major challenge. Objective: This paper aims to understand how software developers experience performance in a highly volatile environment. This understanding could be used as a basis for guiding formation and maintenance of high-performing teams. Method: A qualitative multiple-case study using thematic interviews was conducted with 16 experienced practitioners in five organisations. Results: We found 33 major categories of performance factors, arranged as a theoretical structure that explains how the subjects experience software team performance. Conclusions: Based on our study, software teams are engaged in a constant cycle of interpreting their performance and aligning it with other stakeholders. Enhancing performance experiences requires integration of soft factors, such as communication, team spirit, and team identity, into the overall development process.

This study presents the results of a systematic mapping study on software size estimation metrics and methods. The methods are investigated based on the entity type(s) measured by the method, size attribute type, size measure(s), and theoretical/empirical validation status of the method, and restriction with respect to functional domain type(s) and software development methodology applicability. Based on the results of the survey, we provide a discussion on the current state of the art in software sizing.

We present our ongoing work building a Raspberry Pi cluster consisting of 300 nodes. The unique characteristics of this single board computer pose several challenges, but also offer a number of interesting opportunities. On the one hand, a single Raspberry Pi can be purchased cheaply and has a low power consumption, which makes it possible to create an affordable and energy-efficient cluster. On the other hand, it lacks in computing power, which makes it difficult to run computationally intensive software on it. Nevertheless, by combining a large number of Raspberries into a cluster, this drawback can be (partially) offset. Here we report on the first important steps of creating our cluster: how to set up and configure the hardware and the system software, and how to monitor and maintain the system. We also discuss potential use cases for our cluster, the two most important being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.

In this paper, we propose a method for predicting development effort based on user stories. Such approach is well suited for Agile software projects where requirements are developed along with the project and only sketched in a rough manner. We apply the proposed method to two industrial Agile software projects of very different size and structure. We show that such effort estimation works reasonably well if user stories are written in a structured way.

The pertinent mission of software project management is to continuously achieve more and more successful projects. In the field of software development, the Kanban method has gained momentum recently, mostly due to its linkages to Lean thinking. However, only a few empirical studies investigate the dynamics and impacts of Kanban on projects. The aim of this study is to improve the understanding on how Kanban impacts on software project work. For the purpose of the study, a framework is developed and empirically investigated in an experimental software R&D setting called Software Factory. The impact of Kanban is evaluated from nine theoretically derived perspectives. The results highlight new findings regarding the application of Kanban in the software context. This bears managerial implications, which is addressed. The key implications of the findings suggest that Kanban and its inherent simplicity motivate the workers and control the project activities.

Agile systems development methods emerged as a response to the inability of previous plan-driven approaches to handle rapidly changing environments (Highsmith, 2002). Originating from the so-called...

Estimation of development effort without imposing overhead on the project and the development team is of paramount importance for any software company. This study proposes a new effort estimation methodology aimed at agile and iterative development environments not suitable for description by traditional prediction methods. We propose a detailed development methodology, discuss a number of architectures of such models (including a wealth of augmented regression models and neural networks) and include a thorough case study of Extreme Programming (XP) in two semi-industrial projects. The results of this research evidence that in the XP environment under study the proposed incremental model outperforms traditional estimation techniques most notably in early phases of development. Moreover, when dealing with new projects, the incremental model can be developed from scratch without resorting itself to historic data.

Test-driven development (TDD) is a programming technique in which the tests are written prior to the source code. It is proposed that TDD is one of the most fundamental practices enabling the development of software in an agile and iterative manner. Both the literature and practice suggest that TDD practice yields several benefits. Essentially, it is claimed that TDD leads to an improved software design, which has a dramatic impact on the maintainability and further development of the system. The impact of TDD on program design has seldom come under the researchers' focus. This paper reports the results from a comparative case study of three software development projects where the effect of TDD on program design was measured using object-oriented metrics. The results show that the effect of TDD on program design was not as evident as expected, but the test coverage was significantly superior to iterative test-last development.

Pair programming is a programming technique in which two programmers use one computer to work together on the same task. There is an ongoing debate over the value of pair programming in software development. The current body of knowledge in this area is scattered and unorganized. Review shows that most of the results have been obtained from experimental studies in university settings. Few, if any, empirical studies exist, where pair programming has been systematically under scrutiny in real software development projects. Thus, its proposed benefits remain currently without solid empirical evidence. This paper reports results from four software development projects where the impact of pair programming on software product quality was studied. Our empirical findings appear to offer contrasting results regarding some of the claimed benefits of pair programming. They indicate that pair programming may not necessarily provide as extensive quality benefits as suggested in literature, and on the other hand, does not result in consistently superior productivity when compared to solo programming.

A classic question in software development is "How much testing is enough?" Aside from dynamic coverage-based metrics, there are few measures that can be used to provide guidance on the quality of an automatic test suite as development proceeds. This paper utilizes the software testing and reliability early warning (STREW) static metric suite to provide a developer with indications of changes and additions to their automated unit test suite and code for added confidence that product quality will be high. Retrospective case studies to assess the utility of using the STREW metrics as a feedback mechanism were performed in academic, open source and industrial environments. The results indicate at statistically significant levels the ability of the STREW metrics to provide feedback on important attributes of an automatic test suite and corresponding code

Software development is always a challenging undertaking and it requires high commitments from individuals who participate in it. Software development often involves new technology, challenging or unknown requirements, and tight schedules - making it particularly prone to several types of risk. These challenges are even more paramount in agile development and in distributed development, where the need for efficient information sharing is important, yet the distributed development makes it very difficult. This paper uses innovative learning methods to explore and to learn about these challenges and how to deal with them. The paper is partially based on presentations given by authors, but a major element in the paper is the case study that is introduced and in which will involve all the participants. The learning in the paper is strongly facilitated by participants' discussions and the insights generated in concrete problem solving situations.

Agile software development methods have caught the attention of software engineers and researchers worldwide. Scientific research is yet scarce. This paper reports results from a study, which aims to organize, analyze and make sense out of the dispersed field of agile software development methods. The comparative analysis is performed using the method's life-cycle coverage, project management support, type of practical guidance, fitness-for-use and empirical evidence as the analytical lenses. The results show that agile software development methods, without rationalization, cover certain/different phases of the software development life-cycle and most of them do not offer adequate support for project management. Yet, many methods still attempt to strive for universal solutions (as opposed to situation appropriate) and the empirical evidence is still very limited. Based on the results, new directions are suggested In principal, it is suggested to place emphasis on methodological quality - not method quantity.

It has been well-established in the software process improvement (SPI) literature and practice that, without commitment from all organizational levels to SPI, the initiative will most likely fail or the results will not be far-reaching. In this paper, the 'commitment' construct is explored, and three forms of commitment are introduced: affective, continuance and normative commitment. Analysis shows that current models of commitment development lack scientific validity and are based on four misconceptions: (1) the assumption of linearity of the human cognitive process (i.e. commitment in this case), (2) the controllability of this process, (3) the notion of a singular commitment construct, and (4) the sole utility perspective on the commitment phenomenon. Implications of these findings for SPI research and practice are discussed.