A Unified Metamodel for Assessing and Predicting Software Evolvability Quality

Software quality is a key assessment factor for organizations to determine the ability of software ecosystems to meet the constantly changing requirements. Many quality models exist that capture and assess the changing factors affecting the quality of a software product. Common to these models is that they, contrary to the software ecosystems they are assessing, are not evolvable or reusable. The thesis first defines what constitutes a unified, evolvable, and reusable quality metamodel. We then introduce SE-EQUAM, a novel, ontological, quality assessment metamodel that was designed from the ground up to support quality unification, reuse, and evolvability. We then validate the reus-ability of our metamodel through instantiating a domain specific quality assessment model called OntEQAM that assesses evolvability as a non-functional software quality based on product and com-munity dimensions. A fuzzy logic based assessment process that addresses uncertainties around score boundaries supports the evolvability quality assessment. The presented assessment process also uses the unified representation of the input knowledge artifacts, the metamodel, and the model to provide a fuzzy assessment score. Finally, we further interpret and predict the evolvability as-sessment scores using a novel, cross-disciplinary approach that re-applies financial technical analy-sis, which are indicators, and patterns typically used for price analysis and the forecasting of stocks in financial markets. We performed several case studies to illustrate and evaluate the applicability of our proposed evolvability score prediction approach

Strategy-focused architecture investment decisions

The thesis considers how a systematic approach for evaluating architecture investments can lead to decisions that are driven by business preferences rather than by personal incentives. A decision to invest in software-architecture requires systematic evaluation of the trade-off between strategic long-term benefits of architecture on the business and short term investment. It typically is a decision that is difficult to explain and quantify. In this sense, it is not surprising that such a decision is often driven by personal incentives or strong leadership of deciders, architects and managers, leading to suboptimal decision-making process in the organization. This PhD thesis proposes a way in which to support the decision to invest in architecture by linking the architecture improvements to the business strategy and taking into consideration the human aspects. We follow the iterative study design process including several real-life case studies, multiple interviews, and an experiment. In the first case study, we investigate how practitioners make a decision on architecture investment with a focus on how the decision process can be improved in industrial practice. To support the decision process in an objective way, we propose to use arguments based on real options theory. The evaluation by practitioners disclosed that including such economics of architecture is necessary but not sufficient for decision making. To better understand the information needs for decision making we conducted field interviews on the kinds of information that architects and managers need. In a subsequent experiment we tested whether which kind of information is actually used in decision making. As expected, the professionals tend to use just a few information types for decision making. However, our results suggest that additional quantified information was used by participants with longer development experience and under time pressure. Based upon the experimental findings we propose a concept to quantify the customer value of architecture. Despite the positive evaluation, the practitioners asked for further improvements to translate the architecture changes directly to the economic value. Ultimately, based on the findings from the preceding studies we propose a comprehensive approach to support objective architecture decision making; we label it Strategy-focused Architecture (StArch). Adopting strategic management tools, strategy map and balanced scorecards, we provide step-by-step guidance to assess the economic benefits of architecture improvements aligned with the strategic business objectives

Products, Platforms, and Open Innovation: Three Essays on Technology Innovation

High technology industries, where IT artifacts are core to the business model of a firm, are marked by a high level of market competition and uncertainty. Firms within these industries are constantly evolving at a swift pace. Products and services developed in these industries have the shortest life cycle from product development to maturity, compared to those developed in other industries. According to a 2015 KPMG report, products and services in the high technology industry have an average maturity life cycle of 0.5 - 5 years, which is the shortest among all sectors (KPMG, 2015). Value generation and capture from these products and services must happen in a shorter duration compared to those from other industries. Imitation of products and services in these industries is also rampant, diminishing opportunities to generate value from innovative products and services. According to extant research, imitation among vendors in the IT sector is widespread, and firms mimic direct competitors in the introduction and withdrawal of products and services (Ruckman et al., 2015; Rhee et al., 2006). While the inherent nature of products developed in the IT industry and the associated incremental innovation leads to better performance gains, these gains erode quickly via imitation from firms competing in the same domain (Ethiraj et al., 2008). For many firms, these issues lead to a shift in their revenue generation model. Rather than appropriating the value from direct sales of products and services, firms have slowly started opting for innovation strategies that allow rent-seeking through opening up the business and revenue models of the firm. These strategies may include but are not limited to, adopting open standards for their products and services, establishing platform business models and engaging in open innovation. In this thesis, I assess these three innovation strategies and their value to a firm in terms of product and services and related value performance. In the first essay of this thesis, I start by examining the lifecycle of products in information technology-intensive firms, which is deemed to be shorter compared to other industries. I call these products complex assembled digital products (CADP). In the product innovation literature, the emergence of a dominant design configuration in a product category is seen as the start of a technological lifecycle that allows winners of the industry to appropriate long-term returns through incremental innovation. In the context of a complex assembled digital product, a dominant design will manifest itself as a single dominant design configuration or a narrow set of configurations that represent a majority of the products manufactured in a product category (Tushman & Murmann, 1998; Cecere et al., 2015). However, in technology-intensive firms, two challenges need further exploration. Firstly, due to the pace of innovation in technology-intensive industries, it is highly likely that a dominant design configuration never emerges (Srinivasan et al., 2006). Secondly, due to the modular nature of the products, even if a dominant design is achieved, it is achieved at the configurational level. It manifests itself as the set of components that achieves dominance in a product configuration (Murmann & Frenken, 2006). In the first essay, I examine the evolutionary attributes of the components of a CADP, which enable the components to become and remain part of the dominant design configuration of the product for a longer duration. I model the entry and survival of a component in a dominant design configuration using three evolutionary attributes: (1) pleiotropy of the component, (2) openness of the standard supporting the component, and (3) innovation source of the component. Pleiotropy as a construct is adapted from evolutionary biology and defined as the number of functionalities supported by a component. The standard supporting a component can be open or proprietary. The innovation source can be internal to the industry or external. I empirically test my hypotheses using a rich, longitudinal dataset of TV models spanning 15 years (2002-2016). The results show that components that have higher pleiotropy and that are supported by open standards not only have a higher chance of being selected into the dominant design configuration of TVs but also remain in the TV market for a longer time. However, while components developed through endogenous innovation efforts were nearly four times more likely to enter the dominant design configuration of TVs, their longevity was not significantly different from that of the components sourced exogenously. In the first essay, I look at how adopting components with specific sets of attributes allows firms to win a product market and appropriate value for a long duration from product development. In the second essay, I shift my focus from a product-based business model to a platform business model as an innovation strategy to achieve a competitive advantage. In recent years we have observed the emergence of platform businesses across domains of information technology-intensive industries (van Alystyne and Parker 2016). Firms are either completely shifting to platform business models or starting to include platform business models as part of their business strategy portfolios. Newer firms in these industries are more likely to adopt a platform business model as the core model for value generation and value capture. Seven of the ten most valuable companies in the world have opted for a platform business model as part of their overall business strategy (Cusumano et al., 2019). However, not all firms adopting the platform business model succeed in dominating the market. An exploratory study examined the success of platform businesses in terms of the number of years the firm remained in business. Taking a 20 years dataset of the firms in US markets, it was observed that only 43 out of 252 platform firms flourished are still active (Yoffie et al., 2019). Most of the surviving firms have to spend a considerable amount of resources in incentivizing the stakeholders of the platform, R&D, and marketing activities to stay relevant in the market (Cusumano, 2020). In Essay Two, I investigate the effect of a platform innovation on a firm’s performance under competitive threats. As argued earlier, technology-intensive firms operate in an ever-changing environment where competition is continuously evolving and mimicking the products of the focal firm. This constantly evolving product market competition is inherent in high technology industries. While product market competition encourages the overall pace of innovation as seen in technology-intensive industries, we are not aware of its effect on value generated by the firms operating in those industries. In the second Essay, I model the effect of product market competition on a firm’s performance. I look at how adopting a platform business model mitigates the effect of product market competition on a firm’s value generation. I use a machine learning-based firm classification method to measure the business model adopted by a firm. I extracted data from 10-K annual reports of the sample firms and classified the firms as platform or non-platform based on the supervised classification of 10-K annual reports of the firm. Using a 20-year panel of the firm’s financial data and their business classification, I explore the effect of a platform business model on a firm’s performance under high product market competition. My results suggest that adopting a platform business model can be an effective business strategy in delivering better value in general and under high market competition in particular. A third innovation strategy that has found favor with firms in recent years to build a competitive advantage over rivals is engaging in open innovation. Open innovation is defined as “a paradigm that assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as the firms look to advance their technology” (Chesbrough, 2003). In the context of information technology-intensive firms, open innovation manifests itself in many ways. In recent years, for-profit firms have started engaging with open-source communities to develop products and services on social coding platforms like GitHub. According to my investigation, 41 of the top 100 firms by market valuation have a direct presence on GitHub and actively develop their products with support from open-source developer communities. Opening up open software products and services for the world is another way that allows for faster development and propagation of products across user and developer communities (Khan, 2018). Firms also sponsor open source community developed products and regularly sponsor summer coding schools and hackathons (Mitchell, 2012). These open innovation events have shown promise in the collaborative development of products and services (Tereweisch and Xu, 2008). Firms appropriate rents by selling complementary services for the products they are developing as open-source. In his famous 1997 book, “The Cathedral and the Bazaar,” Eric Raymond coined the term “Cathedral” model of software development to represent the closed sourced, hierarchical and proprietary model of software development and “Bazaar” to represent the open-source, free and equality based software development model (Raymond, 1997). However, there is limited empirical evidence to suggest that firms create and capture value on open innovation platforms like GitHub (West et al., 2014). We do know that firms have started selective revealing of their accumulated knowledge and started engaging with open source communities (Fosfuri et al., 2008; Henkel et al., 2014; Alexy et al., 2018). In the third Essay, I investigate the effect of open-source engagement on the economic outcomes of a firm. More specifically, I look at how engagement on the open-source platform and intensity of that engagement influence the financial performance of a firm. To investigate the influence of open-source innovation on a firm’s financial performance, I created a data set containing all continuous open-source engagements of firms in high technology sectors. I collected this data from multiple sources, including GitHub, 10-K reports, and a search of innovation contests organized by firms. I then matched this data set with the financial information of the firms. I employed the generalized synthetic control method (GSynth) to estimate the model. I estimated the dynamic panel data regression model to measure the influence of open-source engagement intensity on financial performance. Additionally, I also investigated the heterogeneity in the effect of open-source engagement on the financial performance of the firm using the random causal forest. My results suggest that open-source engagement and its intensity positively influence the financial performance of a firm. The effects are heterogeneous and based on the absorptive capacity of the firm, market competition, and other environmental factors. I explore and discuss the implications of my findings on open-source engagement choices by firms. Finally, I conclude this dissertation with the findings of my essays and their implications on information technology-intensive firms. I provide additional details about my studies in the Appendices. The Appendices also highlight the additional analysis done during the research to test the robustness of the results. Overall, this dissertation has broader implications for research and practice alike. There are opportunities for future research and investigation into various innovation strategies adopted by firms in high technology industries. This research also provides directions for applying novel research methods, like the generalized synthetic control method and machine learning algorithms, in IS research

ICSEA 2022: the seventeenth international conference on software engineering advances

The Seventeenth International Conference on Software Engineering Advances (ICSEA 2022), held between October 16th and October 20th, 2022, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. Several tracks were proposed to treat the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learned. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. Other advanced aspects are related to on-time practical aspects, such as run-time vulnerability checking, rejuvenation process, updates partial or temporary feature deprecation, software deployment and configuration, and on-line software updates. These aspects trigger implications related to patenting, licensing, engineering education, new ways for software adoption and improvement, and ultimately, to software knowledge management. There are many advanced applications requiring robust, safe, and secure software: disaster recovery applications, vehicular systems, biomedical-related software, biometrics related software, mission critical software, E-health related software, crisis-situation software. These applications require appropriate software engineering techniques, metrics and formalisms, such as, software reuse, appropriate software quality metrics, composition and integration, consistency checking, model checking, provers and reasoning. The nature of research in software varies slightly with the specific discipline researchers work in, yet there is much common ground and room for a sharing of best practice, frameworks, tools, languages and methodologies. Despite the number of experts we have available, little work is done at the meta level, that is examining how we go about our research, and how this process can be improved. There are questions related to the choice of programming language, IDEs and documentation styles and standard. Reuse can be of great benefit to research projects yet reuse of prior research projects introduces special problems that need to be mitigated. The research environment is a mix of creativity and systematic approach which leads to a creative tension that needs to be managed or at least monitored. Much of the coding in any university is undertaken by research students or young researchers. Issues of skills training, development and quality control can have significant effects on an entire department. In an industrial research setting, the environment is not quite that of industry as a whole, nor does it follow the pattern set by the university. The unique approaches and issues of industrial research may hold lessons for researchers in other domains. We take here the opportunity to warmly thank all the members of the ICSEA 2022 technical program committee, as well as all the reviewers. The creation of such a high-quality conference program would not have been possible without their involvement. We also kindly thank all the authors who dedicated much of their time and effort to contribute to ICSEA 2022. We truly believe that, thanks to all these efforts, the final conference program consisted of top-quality contributions. We also thank the members of the ICSEA 2022 organizing committee for their help in handling the logistics of this event. We hope that ICSEA 2022 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in software engineering advances

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

From Resilience-Building to Resilience-Scaling Technologies: Directions -- ReSIST NoE Deliverable D13

This document is the second product of workpackage WP2, "Resilience-building and -scaling technologies", in the programme of jointly executed research (JER) of the ReSIST Network of Excellence. The problem that ReSIST addresses is achieving sufficient resilience in the immense systems of ever evolving networks of computers and mobile devices, tightly integrated with human organisations and other technology, that are increasingly becoming a critical part of the information infrastructure of our society. This second deliverable D13 provides a detailed list of research gaps identified by experts from the four working groups related to assessability, evolvability, usability and diversit