Pekka Abrahamsson

Zeeshan Rasheed, Muhammad Waseem, Kai-Kristian Kemell et al.

Context: LLM-based multi-agent systems enable automation and decision support in software development, yet existing studies rely on benchmark datasets offering only binary pass-or-fail results, limiting insight into real-world applicability. Objective: This study empirically investigates the potential and limitations of LLM-based agents in autonomous software development tasks. Method: A two-phase approach was employed: developing a multi-agent system, CodePori, for automated code generation, and conducting participant-based evaluation to assess practical performance. Results: Participant feedback reveals key strengths, challenges, and areas for improvement in LLM-based multi-agent systems, highlighting aspects missed by standard code-generation benchmarks. Conclusions: While LLM-based multi-agent systems show potential for large-scale software development, successful integration requires addressing challenges such as memory limitations, hallucinations, and code smells, alongside a practitioner-centric perspective.

Ayman Asad Khan, Md Toufique Hasan, Kai Kristian Kemell et al.

This paper presents an experience report on the development of Retrieval Augmented Generation (RAG) systems using PDF documents as the primary data source. The RAG architecture combines generative capabilities of Large Language Models (LLMs) with the precision of information retrieval. This approach has the potential to redefine how we interact with and augment both structured and unstructured knowledge in generative models to enhance transparency, accuracy, and contextuality of responses. The paper details the end-to-end pipeline, from data collection, preprocessing, to retrieval indexing and response generation, highlighting technical challenges and practical solutions. We aim to offer insights to researchers and practitioners developing similar systems using two distinct approaches: OpenAI's Assistant API with GPT Series and Llama's open-source models. The practical implications of this research lie in enhancing the reliability of generative AI systems in various sectors where domain-specific knowledge and real-time information retrieval is important. The Python code used in this work is also available at: https://github.com/GPT-Laboratory/RAG-LLM-Development-Guidebook-from-PDFs.

Anh Nguyen-Duc, Daniela S. Cruzes, Snarby Terje et al.

[Background] An increasing number of commercial firms are participating in Open Source Software (OSS) projects to reduce their development cost and increase technical innovativeness. When collaborating with other firms whose sought values are conflicts of interests, firms may behave uncooperatively leading to harmful impacts on the common goal. [Aim] This study explores how software firms both collaborate and compete in OSS projects. [Method] We adopted a mixed research method on three OSS projects. [Result] We found that commercial firms participating in community-initiated OSS projects collaborate in various ways across the organizational boundaries. While most of firms contribute little, a small number of firms that are very active and account for large proportions of contributions. We proposed a conceptual model to explain for coopetition among software firms in OSS projects. The model shows two aspects of coopetition can be managed at the same time based on firm gatekeepers. [Conclusion] Firms need to operationalize their coopetition strategies to maximize value gained from participating in OSS projects.

José Antonio Siqueira de Cerqueira, Mamia Agbese, Rebekah Rousi et al.

AI-based systems, including Large Language Models (LLM), impact millions by supporting diverse tasks but face issues like misinformation, bias, and misuse. AI ethics is crucial as new technologies and concerns emerge, but objective, practical guidance remains debated. This study examines the use of LLMs for AI ethics in practice, assessing how LLM trustworthiness-enhancing techniques affect software development in this context. Using the Design Science Research (DSR) method, we identify techniques for LLM trustworthiness: multi-agents, distinct roles, structured communication, and multiple rounds of debate. We design a multi-agent prototype LLM-MAS, where agents engage in structured discussions on real-world AI ethics issues from the AI Incident Database. We evaluate the prototype across three case scenarios using thematic analysis, hierarchical clustering, comparative (baseline) studies, and running source code. The system generates approximately 2,000 lines of code per case, compared to only 80 lines in baseline trials. Discussions reveal terms like bias detection, transparency, accountability, user consent, GDPR compliance, fairness evaluation, and EU AI Act compliance, showing this prototype ability to generate extensive source code and documentation addressing often overlooked AI ethics issues. However, practical challenges in source code integration and dependency management may limit its use by practitioners.

Md Toufique Hasan, Muhammad Waseem, Kai-Kristian Kemell et al.

Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accuracy and contextual relevance. However, there is a lack of empirical studies that report on the development of RAG-based implementations grounded in real-world use cases, evaluated through general user involvement, and accompanied by systematic documentation of lessons learned. This paper presents five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics. Each system incorporates multilingual OCR, semantic retrieval via vector embeddings, and domain-adapted LLMs, deployed through local servers or cloud APIs to meet distinct user needs. A web-based evaluation involving a total of 100 participants assessed the systems across six dimensions: (i) Ease of Use, (ii) Relevance, (iii) Transparency, (iv) Responsiveness, (v) Accuracy, and (vi) Likelihood of Recommendation. Based on user feedback and our development experience, we documented twelve key lessons learned, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.

Tomas Herda, Victoria Pichler, Zheying Zhang et al.

The full-day workshop on AI and Agile at XP 2025 convened a diverse group of researchers and industry practitioners to address the practical challenges and opportunities of integrating Artificial Intelligence into Agile software development. Through interactive sessions, participants identified shared frustrations related to integrating AI into Agile Software Development practices, including challenges with tooling, governance, data quality, and critical skill gaps. These challenges were systematically prioritized and analyzed to uncover root causes. The workshop culminated in the collaborative development of a research roadmap that pinpoints actionable directions for future work, including both immediate solutions and ambitious long-term goals. The key outcome is a structured agenda designed to foster joint industry-academic efforts to move from identified frustrations to successful implementation.

Muhammad Waseem, Aakash Ahmad, Kai-Kristian Kemell et al.

Vibe Coding (VC) is a form of software development assisted by generative AI, in which developers describe the intended functionality or logic via natural language prompts, and the AI system generates the corresponding source code. VC can be leveraged for rapid prototyping or developing the Minimum Viable Products (MVPs); however, it may introduce several risks throughout the software development life cycle. Based on our experience from several internally developed MVPs and a review of recent industry reports, this article analyzes the flow-debt tradeoffs associated with VC. The flow-debt trade-off arises when the seamless code generation occurs, leading to the accumulation of technical debt through architectural inconsistencies, security vulnerabilities, and increased maintenance overhead. These issues originate from process-level weaknesses, biases in model training data, a lack of explicit design rationale, and a tendency to prioritize quick code generation over human-driven iterative development. Based on our experiences, we identify and explain how current model, platform, and hardware limitations contribute to these issues, and propose countermeasures to address them, informing research and practice towards more sustainable VC approaches.

Zheying Zhang, Tomas Herda, Victoria Pichler et al.

This paper synthesizes the key findings from a full-day XP2025 workshop on "AI and Agile: From Frustration to Success", held in Brugg-Windisch, Switzerland. The workshop brought together over 30 interdisciplinary academic researchers and industry practitioners to tackle the concrete challenges and emerging opportunities at the intersection of Generative Artificial Intelligence (GenAI) and agile software development. Through structured, interactive breakout sessions, participants identified shared pain points like tool fragmentation, governance, data quality, and critical skills gaps in AI literacy and prompt engineering. These issues were further analyzed, revealing underlying causes and cross-cutting concerns. The workshop concluded by collaboratively co-creating a multi-thematic research roadmap, articulating both short-term, implementable actions and visionary, long-term research directions. This cohesive agenda aims to guide future investigation and drive the responsible, human-centered integration of GenAI into agile practices.

Zheying Zhang, Maruf Rayhan, Tomas Herda et al.

In agile software development, maintaining high-quality user stories is crucial, but also challenging. This study explores the use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams. We developed a reference model for an Autonomous LLM-based Agent System and implemented it at the company. The quality of user stories in the study and the effectiveness of these agents for user story quality improvement was assessed by 11 participants across six agile teams. Our findings demonstrate the potential of LLMs in improving user story quality, contributing to the research on AI role in agile development, and providing a practical example of the transformative impact of AI in an industry setting.

Rebekah Rousi, Hooman Samani, Niko Mäkitalo et al.

Business and technology are intricately connected through logic and design. They are equally sensitive to societal changes and may be devastated by scandal. Cooperative multi-robot systems (MRSs) are on the rise, allowing robots of different types and brands to work together in diverse contexts. Generative artificial intelligence has been a dominant topic in recent artificial intelligence (AI) discussions due to its capacity to mimic humans through the use of natural language and the production of media, including deep fakes. In this article, we focus specifically on the conversational aspects of generative AI, and hence use the term Conversational Generative artificial intelligence (CGI). Like MRSs, CGIs have enormous potential for revolutionizing processes across sectors and transforming the way humans conduct business. From a business perspective, cooperative MRSs alone, with potential conflicts of interest, privacy practices, and safety concerns, require ethical examination. MRSs empowered by CGIs demand multi-dimensional and sophisticated methods to uncover imminent ethical pitfalls. This study focuses on ethics in CGI-empowered MRSs while reporting the stages of developing the MORUL model.

Zeeshan Rasheed, Muhammad Waseem, Kari Systä et al.

As Large Language Models (LLMs) have become integral to both research and daily operations, rigorous evaluation is crucial. This assessment is important not only for individual tasks but also for understanding their societal impact and potential risks. Despite extensive efforts to examine LLMs from various perspectives, there is a noticeable lack of multi-agent AI models specifically designed to evaluate the performance of different LLMs. To address this gap, we introduce a novel multi-agent AI model that aims to assess and compare the performance of various LLMs. Our model consists of eight distinct AI agents, each responsible for retrieving code based on a common description from different advanced language models, including GPT-3.5, GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, Google Bard, LLAMA, and Hugging Face. Our developed model utilizes the API of each language model to retrieve code for a given high-level description. Additionally, we developed a verification agent, tasked with the critical role of evaluating the code generated by its counterparts. We integrate the HumanEval benchmark into our verification agent to assess the generated code's performance, providing insights into their respective capabilities and efficiencies. Our initial results indicate that the GPT-3.5 Turbo model's performance is comparatively better than the other models. This preliminary analysis serves as a benchmark, comparing their performances side by side. Our future goal is to enhance the evaluation process by incorporating the Massively Multitask Benchmark for Python (MBPP) benchmark, which is expected to further refine our assessment. Additionally, we plan to share our developed model with twenty practitioners from various backgrounds to test our model and collect their feedback for further improvement.

Arif Ali Khan, Aakash Ahmad, Muhammad Waseem et al.

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, etc., 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) modeling 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 modeling 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 customize 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.

Anh Nguyen-Duc, Dron Khanna, Des Greer et al.

[Context] The COVID-19 pandemic has had a disruptive impact on how people work and collaborate across all global economic sectors, including the software business. While remote working is not new for software engineers, forced Work-from-home situations to come with both constraints, limitations, and opportunities for individuals, software teams and software companies. 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] The goal of this study is to gain insights on how their WFH environment impacts software projects and software companies. We are also interested in understanding if the impact differs between software startups and established companies. [Method] We conducted a global-scale, cross-sectional survey during spring and summer 2021. Our results are based on quantitative and qualitative analysis of 297 valid responses. [Results] We observed a mixed perception of the impact of WFH on software project management, resilience, and innovation. Certain patterns on WFH, control and coordination mechanisms and collaborative tools are observed globally. We find that team, agility and leadership are the three most important factors for achieving resilience during the pandemic. Although startups do not perceive the impact of WFH differently, there is a difference between engineers who work in a small team context and those who work in a large team context. [Conclusion] The result suggests a contingency approach in studying and improving WFH practices and environment in the future software industry.

Antti Kariluoto, Arto Pärnänen, Joni Kultanen et al.

A common assumption exists according to which machine learning models improve their performance when they have more data to learn from. In this study, the authors wished to clarify the dilemma by performing an empirical experiment utilizing novel vocational student data. The experiment compared different machine learning algorithms while varying the number of data and feature combinations available for training and testing the models. The experiment revealed that the increase of data records or their sample frequency does not immediately lead to significant increases in the model accuracies or performance, however the variance of accuracies does diminish in the case of ensemble models. Similar phenomenon was witnessed while increasing the number of input features for the models. The study refutes the starting assumption and continues to state that in this case the significance in data lies in the quality of the data instead of the quantity of the data.

Mikael Ovaska, Joni Kultanen, Teemu Autto et al.

Sociometric badges are an emerging technology for study how teams interact in physical places. Audio data recorded by sociometric badges is often downsampled to not record discussions of the sociometric badges holders. To gain more information about interactions inside teams with sociometric badges a Voice Activity Detector (VAD) is deployed to measure verbal activity of the interaction. Detecting voice activity from downsampled audio data is challenging because down-sampling destroys information from the data. We developed a VAD using deep learning techniques that achieves only moderate accuracy in a low noise meeting setting and in across variable noise levels despite excellent validation performance. Experiences and lessons learned while developing the VAD are discussed.

Anh Nguyen-Duc, Kai-Kristian Kemell, Pekka Abrahamsson

Context: Software startups are an essential source of innovation and software-intensive products. The need to understand product development in startups and to provide relevant support are highlighted in software research. While state-of-the-art literature reveals how startups develop their software, the reasons why they adopt these activities are underexplored. Objective: This study investigates the tactics behind software engineering (SE) activities by analyzing key engineering events during startup journeys. We explore how entrepreneurial mindsets may be associated with SE knowledge areas and with each startup case. Method: Our theoretical foundation is based on causation and effectuation models. We conducted semi-structured interviews with 40 software startups. We used two-round open coding and thematic analysis to describe and identify entrepreneurial software development patterns. Additionally, we calculated an effectuation index for each startup case. Results: We identified 621 events merged into 32 codes of entrepreneurial logic in SE from the sample. We found a systemic occurrence of the logic in all areas of SE activities. Minimum Viable Product (MVP), Technical Debt (TD), and Customer Involvement (CI) tend to be associated with effectual logic, while testing activities at different levels are associated with causal logic. The effectuation index revealed that startups are either effectuation-driven or mixed-logics-driven. Conclusions: Software startups fall into two types that differentiate between how traditional SE approaches may apply to them. Effectuation seems the most relevant and essential model for explaining and developing suitable SE practices for software startups.

Kai-Kristian Kemell, Atte Elonen, Mari Suoranta et al.

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.

Pekka Abrahamsson, Nilay Oza, Mikko T. Siponen

Although agile software development methods have caught the attention of software engineers and researchers worldwide, scientific research still remains quite scarce. The aim of this study is to order and make sense of the different agile approaches that have been proposed. This comparative review is performed from the standpoint of using the following features as the analytical perspectives: project management support, life-cycle coverage, type of practical guidance, adaptability in actual use, type of research objectives and existence of empirical evidence. The results show that agile software development methods cover, without offering any rationale, different phases of the software development life-cycle and that most of these methods fail to provide adequate project management support. Moreover, quite a few methods continue to offer little concrete guidance on how to use their solutions or how to adapt them in different development situations. Empirical evidence after ten years of application remains quite limited. Based on the results, new directions on agile methods are outlined.

Pekka Abrahamsson

This paper suggests that by operationalizing the concept of commitment in the shape of a model, a new insight is provided in improving software processes - a more human centered approach as opposed to various technical approaches available. In doing so the SPI managers/change agents are able to plan better the software process improvement initiative and benchmark successful projects (as well as failed ones). Results from five interviews with SPI professionals on the proposed Behavior-based Commitment Model are reported, together with early results from the empirical test in 14 software process improvement projects. Early results suggest that the behaviors introduced in the model are relevant in SPI initiatives, the use of model raises the awareness about the people issues in improving processes, and the model could be used aside with CMM, SPICE or other process improvement models. Keywords: software process improvement, commitment, diagnostic tool, self-perception theory.

Pekka Abrahamsson

Several studies have revealed the fact that nearly two-thirds of all software process improvement (SPI) efforts have failed or have at least fallen short of expectations. Literature and practice have shown that commitment to SPI at all organizational levels is essential for the success of any SPI endeavor. A research model for studying the existence, development and interplay of SPI-related commitment is introduced in this paper. This study suggests that software organizations operate through strategic, operational and personal commitment nets. These nets consist of actors, drivers, concerns, actions, commitment, and outcomes. The commitment nets model is applied in a study of four industrial SPI initiatives. The results from two of these cases are reported here. The results show that SPI is driven through the formation and reformation of commitment nets. The contents of strategic, operational and personal commitment nets are laid out and implications are discussed.

Kai-Kristian Kemell, Anh Nguyen-Duc, Xiaofeng Wang et al.

Software Engineering as an industry is highly diverse in terms of development methods and practices. Practitioners employ a myriad of methods and tend to further tailor them by e.g. omitting some practices or rules. This diversity in development methods poses a challenge for software engineering education, creating a gap between education and industry. General theories such as the Essence Theory of Software Engineering can help bridge this gap by presenting software engineering students with higher-level frameworks upon which to build an understanding of software engineering methods and practical project work. In this paper, we study Essence in an educational setting to evaluate its usefulness for software engineering students while also investigating barriers to its adoption in this context. To this end, we observe 102 student teams utilize Essence in practical software engineering projects during a semester long, project-based course.

Kai-Kristian Kemell, Juhani Risku, Arthur Evensen et al.

Software Engineering is an engineering discipline but lacks a solid theoretical foundation. One effort in remedying this situation has been the SEMAT Essence specification. Essence consists of a language for modeling Software Engineering (SE) practices and methods and a kernel containing what its authors describe as being elements that are present in every software development project. In practice, it is a method agnostic project management tool for SE Projects. Using the language of the specification, Essence can be used to model any software development method or practice. Thus, the specification can potentially be applied to any software development context, making it a powerful tool. However, due to the manual work and the learning process involved in modeling practices with Essence, its initial adoption can be tasking for development teams. Due to the importance of project management in SE projects, new project management tools such as Essence are valuable, and facilitating their adoption is consequently important. To tackle this issue in the case of Essence, we present a game-based approach to teaching the use Essence. In this paper, we gamify the learning process by means of an innovative board game. The game is empirically validated in a study involving students from the IT faculty of University of Jyväskylä (n=61). Based on the results, we report the effectiveness of the game-based approach to teaching both Essence and SE project work.

Ville Vakkuri, Pekka Abrahamsson

The growing influence and decision-making capacities of Autonomous systems and Artificial Intelligence in our lives force us to consider the values embedded in these systems. But how ethics should be implemented into these systems? In this study, the solution is seen on philosophical conceptualization as a framework to form practical implementation model for ethics of AI. To take the first steps on conceptualization main concepts used on the field needs to be identified. A keyword based Systematic Mapping Study (SMS) on the keywords used in AI and ethics was conducted to help in identifying, defying and comparing main concepts used in current AI ethics discourse. Out of 1062 papers retrieved SMS discovered 37 re-occurring keywords in 83 academic papers. We suggest that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

Anh Nguyen Duc, Xiaofang Weng, Pekka Abrahamsson

[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 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 adopted limitedly. We also found tactics in four domains (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.

Arthur Evensen, Kai-Kristian Kemell, Xiaofeng Wang et al.

Software Engineering practitioners work using highly diverse methods and practices, and general theories in software engineering are lacking. One attempt at creating a common ground in the area of software engineering methodologies has been the Essence Theory of Software Engineering, which can be considered a method-agnostic project management tool for software engineering. Essence supports the use of any development practices and provides a framework for building a suitable method for any software engineering context. However, Essence presently suffers from low practitioner adoption that is partially considered to be caused by a lack of proper tooling. In this paper, we present Essencery, a tool for essentializing software engineering methods and practices using the Essence graphical syntax. Essencery aims to facilitate adoption of Essence among potential future users. We present an empirical evaluation of the tool by means of a qualitative, quasi-formal experiment and, based on the experiment, confirm that the tool is easy to use and useful for its intended purpose.

Henry Edison, Xiaofeng Wang, Ronald Jabangwe et al.

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.

Anh Nguyen Duc, Pekka Abrahamsson

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.

Nattakarn Phaphoom, Xiaofeng Wang, Sarah Samuel et al.

In the context of cloud computing, risks associated with underlying technologies, risks involving service models and outsourcing, and enterprise readiness have been recognized as potential barriers for the adoption. To accelerate cloud adoption, the concrete barriers negatively influencing the adoption decision need to be identified. Our study aims at understanding the impact of technical and security-related barriers on the organizational decision to adopt the cloud. We analyzed data collected through a web survey of 352 individuals working for enterprises consisting of decision makers as well as employees from other levels within an organization. The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability. The result from our logistic regression analysis confirms the criticality of the security concern, which results in an up to 26-fold increase in the non-adoption likelihood. Our study underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Jussi Ronkainen, Pekka Abrahamsson

Agile software development methods have been suggested as useful in many situations and contexts. However, only few (if any) experiences are available regarding the use of agile methods in embedded domain where the hardware sets tight requirements for the software. This development domain is arguably far away from the agile home ground. This paper explores the possibility of using agile development techniques in this environment and defines the requirements for new agile methods targeted to facilitate the development of embedded software. The findings are based on an empirical study over a period 12 months in the development of low-level telecommunications software. We maintain that by addressing the requirements we discovered, agile methods can be successful also in the embedded software domain.

Pekka Abrahamsson, Outi Salo, Jussi Ronkainen et al.

Agile - denoting "the quality of being agile, readiness for motion, nimbleness, activity, dexterity in motion" - software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being "agile" against the defined criterion. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.

Yaser Ghanam, Frank Maurer, Pekka Abrahamsson

Context: While there are many success stories of achieving high reuse and improved quality using software platforms, there is a need to investigate the issues and challenges organizations face when transitioning to a software platform strategy. Objective: This case study provides a comprehensive taxonomy of the challenges faced when a medium-scale organization decided to adopt software platforms. The study also reveals how new trends in software engineering (i.e. agile methods, distributed development and flat management structures) interplayed with the chosen platform strategy. Method: We used an ethnographic approach to collect data by spending time at a medium-scale company in Scandinavia. We conducted 16 in-depth interviews with representatives of eight different teams, three of which were working on three separate platforms. The collected data was analyzed using Grounded Theory. Results: The findings identify four classes of challenges, namely: business challenges, organizational challenges, technical challenges, and people challenges. The article explains how these findings can be used to help researchers and practitioners identify practical solutions and required tool support. Conclusion: The organization's decision to adopt a software platform strategy introduced a number of challenges. These challenges need to be understood and addressed in order to reap the benefits of reuse. Researchers need to further investigate issues such as supportive organizational structures for platform development, the role of agile methods in software platforms, tool support for testing and continuous integration in the platform context, and reuse recommendation systems.

Tuomo Kähkönen, Pekka Abrahamsson

The relationship between agile methods and Software Engineering Institute's CMM approach is often debated. Some authors argue that the approaches are compatible, while others have criticized the application of agile methods from the CMM perspective. Only few CMM based assessments have been performed on projects using agile approaches. This paper explores an empirical case where a project using Extreme Programming (XP) based approach was assessed using the CMMI framework. The results provide empirical evidence pointing out that it is possible to achieve maturity level 2 with approach based on XP. Yet, the results confirm that XP, as it is defined, is not sufficient. This study demonstrates that it is possible to use the CMMI for assessing and improving agile processes. However, the analysis reveals that assessing an agile organization requires more interpretations than normally would be the case. It is further concluded that the CMMI model does not always support interpretations in an agile context.

Carmine Giardino, Xiaofeng Wang, Pekka Abrahamsson

Software startups are newly created companies with little operating history and oriented towards producing cutting-edge products. As their time and resources are extremely scarce, and one failed project can put them out of business, startups need effective practices to face with those unique challenges. However, only few scientific studies attempt to address characteristics of failure, especially during the early- stage. With this study we aim to raise our understanding of the failure of early-stage software startup companies. This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach. The results present how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework. Despite strategies reveal the first need to understand the problem/solution fit, actual executions prioritize the development of the product to launch on the market as quickly as possible to verify product/market fit, neglecting the necessary learning process.

Daniel Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.

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.

Daniel Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.

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 2220 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.

Daniel Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.

The growing literature on affect among software developers mostly reports on the linkage between happiness, software quality, and developer productivity. Understanding the positive side of happiness -- positive emotions and moods -- is an attractive and important endeavor. Scholars in industrial and organizational psychology have suggested that also studying the negative side -- 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, and is mainly expressed in terms of development productivity and software quality. In this paper, we attempt to uncover the experienced consequences of unhappiness among software developers. Using qualitative data analysis of the responses given by 181 questionnaire participants, we identified 49 consequences of unhappiness while doing software development. We found detrimental consequences on developers' mental well-being, the software development process, and the produced artifacts. Our classification scheme, available as open data, will spawn 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.

Daniel Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.

Recent research in software engineering supports the "happy-productive" thesis, and the desire of flourishing happiness among programmers is often expressed by industry practitioners. Recent literature has suggested that a cost-effective way to foster happiness and productivity among workers could be to limit unhappiness of developers due to its negative impact. However, possible negative effects of unhappiness are still largely unknown in the software development context. In this paper, we present the first results from a study exploring the consequences of the unhappy developers. Using qualitative data analysis of the survey responses given by 181 participants, we identified 49 potential consequences of unhappiness while developing software. These results have several implications. While raising the awareness of the role of moods, emotions and feelings in software development, we foresee that our classification scheme will spawn new happiness studies linking causes and effects, and it can act as a guideline for developers and managers to foster happiness at work.

Amantia Pano, Daniel Graziotin, Pekka Abrahamsson

The increasing popularity of JavaScript has led to a variety of JavaScript frameworks that aim to help developers to address programming tasks. However, the number of JavaScript frameworks has risen rapidly to thousands of versions. It is challenging for practitioners to identify the frameworks that best fit their needs and to develop new ones which fit such needs. Furthermore, there is a lack of knowledge regarding what drives developers towards the choice. This paper explores the factors and actors that lead to the choice of a JavaScript framework. We conducted a qualitative interpretive study of semi-structured interviews. We interviewed 18 decision makers regarding the JavaScript framework selection, up to reaching theoretical saturation. Through coding the interview responses, we offer a model of desirable JavaScript framework adoption factors. The factors are grouped into categories that are derived via the Unified Theory of Acceptance and Use of Technology. The factors are performance expectancy (performance, size), effort expectancy (automatization, learnability, complexity, understandability), social influence (competitor analysis, collegial advice, community size, community responsiveness), facilitating conditions (suitability, updates, modularity, isolation, extensibility), and price value. A combination of four actors, which are customer, developer, team, and team leader, leads to the choice. Our model contributes to the body of knowledge related to the adoption of technology by software engineers. As a practical implication, our model is useful for decision makers when evaluating JavaScript frameworks, as well as for developers for producing desirable frameworks.

Daniel Graziotin, Xiaofeng Wang, Pekka Abrahamsson

Affects---emotions and moods---have an impact on cognitive activities and the working performance of individuals. Development tasks are undertaken through cognitive processes, yet software engineering research lacks theory on affects and their impact on software development activities. In this paper, we report on an interpretive study aimed at broadening our understanding of the psychology of programming in terms of the experience of affects while programming, and the impact of affects on programming performance. We conducted a qualitative interpretive study based on: face-to-face open-ended interviews, in-field observations, and e-mail exchanges. This enabled us to construct a novel explanatory theory of the impact of affects on development performance. The theory is explicated using an established taxonomy framework. The proposed theory builds upon the concepts of events, affects, attractors, focus, goals, and performance. Theoretical and practical implications are given.

Daniel Graziotin, Xiaofeng Wang, Pekka Abrahamsson

The study of affects (i.e., emotions, moods) in the workplace has received a lot of attention in the last 15 years. Despite the fact that software development has been shown to be intellectual, creative, and driven by cognitive activities, and that affects have a deep influence on cognitive activities, software engineering research lacks an understanding of the affects of software developers. This note provides (1) common misconceptions of affects when dealing with job satisfaction, motivation, commitment, well-being, and happiness; (2) validated measurement instruments for affect measurement; and (3) our recommendations when measuring the affects of software developers.

Daniel Graziotin, Xiaofeng Wang, Pekka Abrahamsson

For more than 30 years, it has been claimed that a way to improve software developers' productivity and software quality is to focus on people and to provide incentives to make developers satisfied and happy. This claim has rarely been verified in software engineering research, which faces an additional challenge in comparison to more traditional engineering fields: software development is an intellectual activity and is dominated by often-neglected human aspects. Among the skills required for software development, developers must possess high analytical problem-solving skills and creativity for the software construction process. According to psychology research, affects-emotions and moods-deeply influence the cognitive processing abilities and performance of workers, including creativity and analytical problem solving. Nonetheless, little research has investigated the correlation between the affective states, creativity, and analytical problem-solving performance of programmers. This article echoes the call to employ psychological measurements in software engineering research. We report a study with 42 participants to investigate the relationship between the affective states, creativity, and analytical problem-solving skills of software developers. The results offer support for the claim that happy developers are indeed better problem solvers in terms of their analytical abilities. The following contributions are made by this study: (1) providing a better understanding of the impact of affective states on the creativity and analytical problem-solving capacities of developers, (2) introducing and validating psychological measurements, theories, and concepts of affective states, creativity, and analytical-problem-solving skills in empirical software engineering, and (3) raising the need for studying the human factors of software engineering by employing a multidisciplinary viewpoint.

Stefan Hellweger, Xiaofeng Wang, Pekka Abrahamsson

User Experience (UX) has been a buzzword in agile literature in recent years. However, often UX remains as a vague concept and it may be hard to understand the very nature of it in the context of agile software development. This paper explores the multifaceted UX literature, emphasizes the multi-dimensional nature of the concept and organizes the current state-of-the-art knowledge. As a starting point to better understand the contemporary meaning of UX assigned by practitioners, we selected four UX blogs and performed an analysis using a framework derived from the literature review. The preliminary results show that the practitioners more often focus on interaction between product and user and view UX from design perspective predominantly. While the economical perspective receives little attention in literature, it is evident in practitioners writings. Our study opens up a promising line of request of the contemporary meaning of UX in practice.

Daniel Graziotin, Xiaofeng Wang, Pekka Abrahamsson

Studies show that software developers' happiness pays off when it comes to productivity.

Vaibhavi Oza, Petri Kettunen, Pekka Abrahamsson et al.

This paper presents an empirical study on how self- organized software teams could attain high performance using agile and lean practices. In particular, the paper qualitatively examines characteristics of high performance and self- organization in one project team. The case under study is a customer-driven student project, carried out to develop an alpha-version prototype. The paper also studies how certain agile software practices aid in initialising self-organization in the team. The main results indicate that self-organization as supported by certain Agile and Lean practices helps teams in achieving higher performance.

Sarunas Marciuska, Cigdem Gencel, Pekka Abrahamsson

Market-driven software intensive product development companies have been more and more experiencing the problem of feature expansion over time. Product managers face the challenge of identifying and locating the high value features in an application and weeding out the ones of low value from the next releases. Currently, there are few methods and tools that deal with feature identification and they address the problem only partially. Therefore, there is an urgent need of methods and tools that would enable systematic feature reduction to resolve issues resulting from feature creep. This paper presents an approach and an associated tool to automate feature identification for web applications. For empirical validation, a multiple case study was conducted using three well known web applications: Youtube, Google and BBC. The results indicate that there is a good potential for automating feature identification in web applications.

Pekka Abrahamsson, Karlheinz Kautz, Heikki Sieppi et al.

Emerging agile software development methods are people oriented development approaches to be used by the software industry. The personal software process (PSP) is an accepted method for improving the capabilities of a single software engineer. Five original hypotheses regarding the impact of the PSP to individual performance are tested. Data is obtained from 58 computer science students in three university courses on the master level, which were held in two different educational institutions in Finland and Denmark. Statistical data treatment shows that the use of PSP did not improve size and time estimation skills but that the productivity did not decrease and the resulting product quality was improved. The implications of these findings are briefly addressed.

Petteri Alahuhta, Pekka Abrahamsson, Antti Nummiaho

Lead user driven innovation and open innovation paradigms seek to involve consumers and common people to innovative product development projects. In order to help developers choose ideas that meet the end users' needs, we undertook a massive collaborative research effort and collected 40000 ideas from 2150 common people about future mobile services that they would like to use. We inspired each people to produce tens of mobile service ideas. In this paper we carry out an analysis for 4000 ideas from the idea database. We had a particular interest in whether peoples' ideas can be used in foreseeing the technology development needs. The results show that end users produce ideas that are conservative more than novel. Therefore, we claim that consumers' technology foresight horizon is limited by the existing technological base. The second finding, linked to the previous one, is that the great majority of the ideas that consumers expressed could be realised utilizing existing technologies. The implication of this finding is that the idea database should be an interesting source of ideas for service developers. The third finding of the study, related to the methodology, is that a vast number of ideas can be collected fairly easily but analyzing them cost effectively is a challenge.

Pekka Abrahamsson

Quality managers, change agents and researchers are often troubled in defining and demonstrating the level of success achieved in software process improvement (SPI) initiatives. So far, there exist only few frameworks for identifying the level of success achieved in SPI. Analysis shows that these frameworks do not provide a comprehensive view from all relevant stakeholders involved in SPI. Early results from an ongoing research effort to discover and operationalise success dimensions are reported. Adapted from the project management literature it is suggested that five dimensions characterise the level of success achieved in SPI: (1) project efficiency, (2) impact on the process user, (3) business success, (4) direct operational success and (5) process improvement fit. Results from an empirical analysis are reported where 23 change agents evaluated the relative level of importance of each dimension. Early results indicate that change agents valued the process user satisfaction the most and the process improvement fit the least. This finding confirms the need of having various stakeholders and dimensions acknowledged in a framework that is used to measure the overall success of an SPI initiative.

Xiaofeng Wang, Daniel Graziotin, Juha Rikkilä et al.

Group brainstorming is a well-known idea generation technique, which plays a key role in software development processes. Despite this, the relevant literature has had little to offer in advancing our understanding of the effectiveness of group brainstorming sessions. In this paper we present a research-in-progress on brainstorming while walking, which is a practice built upon the relationship between thinking and walking. The objective is to better understand how to conduct group brainstorming effectively. We compared two brainstorming sessions, one performed during a mountain walk, the other traditionally in a room. Three preliminary findings are obtained: walking can lead to an effective idea generation session; brainstorming while walking can encourage team members to participate in and contribute to the session in an equal manner; and it can help a team to maintain sustainable mental energy. Our study opens up an avenue for future exploration of effective group brainstorming practices.

Daniel Graziotin, Pekka Abrahamsson

As opposed to more mature subjects, software engineering lacks general theories to establish its foundations as a discipline. The Essence Theory of software engineering (Essence) has been proposed by the Software Engineering Methods and Theory (SEMAT) initiative. Essence goal is to develop a theoretically sound basis for software engineering practice and its wide adoption. Essence is yet far from reaching academic and industry adoption. Reasons include a struggle to foresee its utilization potential and the lack of tools implementing it. SEMAT Accelerator (SematAcc) is a Web-positioning tool for a software engineering endeavor, which implements the SEMAT's Essence kernel. SematAcc allows using Essence, thus helping to understand it. The tool enables teaching, adopting, and researching Essence in controlled experiments and case studies.