Business Modeling and Software Design

In the prevailing mechanical-rational view on organisations, business processes and information systems the engineering approach is the road to solutions. A contrasting view can be found in approaches that start from the social character of organisations and/or the role of natural language in information and communication. In this paper I want to examine this discussion from another and perhaps unusual angle: an analysis of the legal contract as representation of a business agreement. Classical contract law considers a contract as a discrete and fully specified business exchange, while Macneil states that such a discrete contract cannot exist and that every contract has relational aspects based on expectations and trust. For Macneil, a contract is not about some fictional discrete exchange, but rather about a mutual agreement of getting things done in a partially undetermined and uncertain future. This debate in law has meaning both for Business Modelling and for Software Design: Business Modelling should be based on how real business is done and not on some rationalistic idealised view on business, and Software Design should not aim at the fictive monolithic integrated system, but on the contrary provide an open structure that allows for human intervention and that allows for integration with other and heterogeneous information flows.

Design Thinking has become popular in the management and innovation context but remains mostly misunderstood, as a result of broad interpretations and the lack of empirical research on the subject. This paper aims to reduce the gap between the academic and industrial perspectives on Design Thinking, by reviewing publications focused on three aspects: (1) studies aimed at defining the concept, (2) empirical case studies about its use or adoption, and (3) models or methods proposed to overcome its main challenges. The existing literature suggests that multiple definitions for Design Thinking coexist with some commonly understood design practices, both among designer and non-designer practitioners alike. The challenge most frequently mentioned is the clash of existing organizational structures with the flexibility and unpredictability of Design Thinking. This paper outlines two different approaches to address such challenge and proposes a definition that brings together the academic and enterprise perspectives of Design Thinking.

There is a growing agreement in academia as well as in practice that business process management (BPM) has become the management discipline for the systematic execution of business strategy. As any other management discipline, it is implemented through a business process itself, the process of process management. Many components of this process of process management are supported through digital technologies. However, there are critical gaps in the digitalization of the BPM process and existing digital components are often not sufficiently integrated. As a result, organizations do not realize the full potential of a BPM-Discipline. This issue is addressed through the BPM-D Application. A key digitalization gap is the transfer of the strategy of an organization into the appropriate portfolio of process improvement initiatives. For top executives this means that they have to be able to take fast well-informed decisions based on strategic priorities. The operational managers need to drive aligned execution activities. This paper discusses an approach towards a process-driven and value-focused portfolio management to create project portfolios that reflect the priorities of the business strategy, combined with a systematic approach to value realization, during a project and especially after project conclusion. As a result, top executive and operational management views are aligned towards the desired outcome. This paper shows how the presented approach is enabled through the BPM-D Application which represents another step towards digital BPM.

Blockchain technology opens up new opportunities for Business Process Management. This is mainly due to its unprecedented capability to let transactions be automatically executed and recorded by Smart Contracts in multi-peer environments, in a decentralised fashion and without central authoritative players to govern the workflow. In this way, blockchains also provide traceability. Traceability of information plays a pivotal role particularly in those supply chains where multiple parties are involved and rigorous criteria must be fulfilled to lead to a successful outcome. In this paper, we investigate how to run a business process in the context of a supply chain on a blockchain infrastructure so as to provide full traceability of its run-time enactment. Our approach retrieves information to trace process instances execution solely from the transactions written on-chain. To do so, hash-codes are reverse-engineered based on the Solidity Smart Contract encoding of the generating process. We show the results of our investigation by means of an implemented software prototype, with a case study on the reportedly challenging context of the pharmaceutical supply chain.

Supply chain management is hampered by a lack of information sharing among partners. Information is not shared as organizations in the supply chain do not have direct contact and/or do not want to share competitive and privacy sensitive information. In addition, companies are often part of multiple supply chains and trading partners vary over time. Blockchains are distributed ledgers in which all parties in a network can have access to data under certain conditions. Private blockchains can be used to support parties in making their demand data directly available to all other parties in their supply chain. These parties can use this data to improve their planning and reduce the bullwhip effect. However, the transparency that blockchain technology offers makes it more difficult to protect sensitive data. The dynamics between these properties are not well understood. In this paper, we design and evaluate a blockchain architecture to explore its feasibility for reducing information asymmetry, while at the same time protecting sensitive data. We found that blockchain technology can allow parties to balance their need for inventory management with their need for flexibility for changing partners. However, measures to protect sensitive data lead either to reduced information, or to reduced speed by which the information can be accessed.

One impact of the digital transformation of industry is an increasing automation of business processes (BPs) and the accompanying need for business process modeling (BPM) and comprehension. The subsequent increased number of processes and process variants to cover all cases, their deeper integration with information services, and additional process structural complexity affects comprehensibility. While virtual reality (VR) has made inroads in other domains and become readily accessible as a graphical interface alternative, its potential for addressing upcoming BPM comprehension challenges has not been sufficiently explored. This paper contributes a solution for visualizing, navigating, interacting with, and annotating business process modeling notation (BPMN) models in VR. An implementation shows its feasibility and an empirical study evaluates the effectiveness, efficiency, and intuitiveness versus alternative model depiction modes.

The collaboration during the modeling process is uncomfortable and characterized by various limitations. Faced with the successful transfer of first process modeling languages to the augmented world, non-transparent processes can be visualized in a more comprehensive way. With the aim to rise comfortability, speed, accuracy and manifoldness of real world process augmentations, a framework for the bidirectional interplay of the common process modeling world and the augmented world has been designed as morphologic box. Its demonstration proves the working of drawn AR integrations. Identified dimensions were derived from (1) a designed knowledge construction axiom, (2) a designed meta-model, (3) designed use cases and (4) designed directional interplay modes. Through a workshop-based survey, the so far best AR modeling configuration is identified, which can serve for benchmarks and implementations.

In this paper, we consider aggregation algorithms for SUM operator for uncertain stream processing. Deterministic algorithms can not be used here because of uncertain data and high rates of data change, time and memory constraints. We compare the most promising available methods. Instead of full distribution functions of query result, we use a set of six parameters based on key moments and quantiles to describe the distributions. It enables us to perform fast recomputations of the aggregation with O(1) complexity. Experimental results demonstrate good performance of uncertain aggregation in comparison to deterministic case. We also found that usage of central limit theorem may be restricted to problems where data satisfy certain conditions.

Service Dominant Logic (SDL) is a mindset that creates many opportunities for designing and innovating networked-business models. One general problem in business model design is the limited support that would guide the operationalization of business models into process-aware information systems (PAIS). This paper proposes a method (namely, SDBMOM) for the operationalization of service-dominant business models into conceptual business process models in BPMN as a first step to the business model implementation. SDBMOM is developed as part of BASE/X business engineering framework that aims to provide conceptual and methodological support for adopting SDL in the end-to-end business design and operationalization. In the development of the SDBMOM, we follow the design-science research methodology, where we defined the problem and set of design goals, developed and designed our artifact, and demonstrated its use. SDBMOM is conceptualized and characterized in the BASE/X framework and presented as a stepwise method that relies on the well-known process modeling approach - BPMN. In this paper, we use an illustrative scenario of travelling service (i.e. TraXP eXecutive) to demonstrate the validity of the method. A structured method which ensures the operationalization of business models as a whole and delineates the operational scope and their boundaries for each value co-creating organization will provide the basis for the specification of conceptual and executable process models, and eventually the implementation as a process-aware information system.

With process models widely used as means for documentation and monitoring of business activities, the conversion into executable software often still remains a manual and time-consuming task. The MAML framework was developed to ease the creation of mobile business apps by jointly modeling process, data, and user interface perspectives in a graphical, process-oriented model for subsequent code generation. However, this domain-specific notation cannot benefit from existing process knowledge which is often encoded in BPMN models. The purpose of this paper is to analyze conceptual differences between both notations from a software development perspective and provide a solution for interoperability through a model-to-model transformation. Therefore, workflow patterns identified in previous research are used to compare both notations. A conceptual mapping of supported concepts is presented and technically implemented using a QVT-O transformation to demonstrate an automated mapping between BPMN and MAML. Consequently, it is possible to simplify the automatic generation of mobile apps by reusing processes specified in BPMN.

The growing use of ICT in public life has coerced the concept of smart cities. In a smart city, numerous physical devices coupled with latest ICT technologies are used by city authorities to provide better services and infrastructure to its citizens. Smart cities have unique security challenges. Owing to the involvement of numerous stakeholders, information security breaches can have wide ranging, long-lasting consequences. The challenges of smart city information security have not received the attention they deserve. This paper combines threat analysis and enterprise architecture modelling to address and mitigate these challenges from a holistic perspective. An information security architecture is presented, which can help stakeholders of the smart city projects to build more secure smart cities.

With regard to context-aware systems: some optimize system-internal processes, based on the context state at hand; others maximize the user-perceived effectiveness of delivered services, by providing different service variants depending on the situation of the user; still others are about offering value-sensitivity when the society demands so. Even though those three perspectives cover a broad range of currently relevant applications there are no widely accepted and commonly used corresponding concepts and terms. This is an obstacle to broadly understand, effectively integrate, and adequately assess such systems. We address this problem, by considering a (component-based) methodological derivation of technical (software) specifications based on underlying enterprise models. That is because context states are about the enterprise environment of a (software) system while the delivery of context-aware services is about technical (software) functionalities; hence, we need a perspective on both. We consider the SDBC (Software Derived from Business Components) approach that brings together enterprise modeling and software specification. On that basis: (a) We deliver a base context-awareness conceptualization; (b) We partially align it to agent technology because adapting behaviors to environments assumes some kind of pro-activity that is only fully covered by agent systems, in our view. We partially illustrate our proposed conceptualization and particularly - the agent technology implications, by means of a case example featuring land border security.

Nowadays, consumer-oriented industries like the Internet of Things, are highly affected by short product cycles and high pricing pressure. Agile and process-oriented organizations are known to perform better in such flexible environments. However, especially industries which are focused on delivering low cost systems are facing big challenges if the according Business Processes are not aligned with the capabilities of the product. Furthermore, non-functional requirements like safety and security are often not integrated in the early stages of a project, but later on added as a kind of extension, leading to a lower product maturity. With this work, we extend our framework for combined variability management in order to create an integrated view on the product variability from an organizational point, as well as from a technical view, including security requirements from the early development.

Developing Internet of Things (IoT) systems is not trivial and needs to be performed systematically to derive an IoT system that meets the required functional and non-functional concerns. Since IoT is applied to different heterogeneous domains usually a one-size-fits-all method is less feasible. For some cases a lightweight method with a few method artefacts are sufficient while in other cases a detailed set of method artefacts over the whole lifecycle might be required. So far, a few IoT system development methods (SDM) have been provided that include the steps necessary for guiding the development of IoT systems but these do not explicitly consider the situational needs for the required IoT method. In this paper we propose a situational method engineering (SME) approach for developing a method base that includes a broad set of method fragments which can be reused to develop customized methods. We illustrate the development of the method base using the existing IoT methods that have been proposed in the literature so far. Further we show how the method base can be used to develop methods for two different cases.

Blockchain technology bears the potential to support the execution of inter-organizational business processes in an efficient way. Furthermore, it addresses various notorious problems of collaboratively designing choreographies and overcoming lack of trust. In this paper, we discuss this potential in more detail and highlight several research challenges that future research has to address towards generic blockchain support for inter-organizational business processes in various application scenarios.

Context: Decisions taken by medical practitioners may be based on explicit and implicit rules. By uncovering these rules, a medical practitioner may have the possibility to explain its decisions in a better way, both to itself and to the person which the decision is affecting.Objective: We investigate if it is possible for a machine learning pipe-line to uncover rules used by medical practitioners, when they decide if a patient could be operated or not. The uncovered rules should have a linguistic meaning.Method: We are evaluating two different algorithms, one of them is developed by us and named “The membership detection algorithm”. The evaluation is done with the help of real-world data provided by a hospital.Results: The membership detection algorithm has significantly better relevance measure, compared to the second algorithm.Conclusion: A machine learning pipe-line, based on our algorithm, makes it possibility to give the medical practitioners an understanding, or to question, how decisions have been taken. With the help of the uncovered fuzzy decision algorithm it is possible to test suggested changes to the feature limits.

Various authors have defined an extensive set of measures regarding the complexity of process design, which can be objectively measured by using structural complexity metrics. Nevertheless, the research in this area indicates that the percentage of empirically and theoretically validated metrics is relatively small. This suggests that there are still no real examples of the metrics usage within organizations. Despite that we could just validate existing proposals, we feel that a new and enhanced approach to evaluate process diagram complexity is feasible and needed. Thus, in the present paper, we will discuss a possibility of developing a new component-based approach for evaluating process diagram complexity, for which we anticipate that it would be more precise than existing ones, since it will also assess such constituent parts of process diagrams which not only contribute to higher complexity (e.g. complex routing behavior), but also to lower complexity (e.g. decomposition). Moreover, we plan to thoroughly validate our approach both in theory and practice.

As the demand for the modeling of knowledge intensive processes grows, so does the necessity to support this task by appropriate models. Adaptive Case Management (ACM) was proposed as appropriate, whereas still confined mostly to theoretical work. This paper compares the execution of BPM process models to ACM models by applying agent based simulations to different processes based on support case handling. The simulation tools applied to the ACM process models were developed in the course of this work. The greater flexibility of ACM models is found to be more effective in processing executions with competent knowledge workers. Another key observation is the possible improvement of average case duration due to parallelization effects.

An incredible amount of data has constantly been generated. There is a gap between the abundance of data and the needs of users. At the same time a particular user needs not only a relevant part of the available data but (s)he needs higher-level (inferred) data in order to use this data in support of decision-making. Analytics comes to bridge that gap, by means of tools. Those tools link what the user needs to what is available as data; generate higher-level (inferred) data based on the raw data; provide visualization that is useful especially to users that have limited knowledge in mathematics and data science. The abundance of definitions that concern analytics as well as the numerous terms and concepts confuse users. Often different concepts are inconsistent with regard to each other. Inspired by this problem, the current paper: provides a systematic analysis featuring analytics in general and business analytics, in particular, developing this also from the perspective of visualization-related needs. We propose a layered business analytics architecture named General Architectural Framework for Business Visual Analytics. The framework presents the main interrelationships between the elements for designing and modeling Business Visual Analytics. We propose a conceptual definition that combines the semantics of the multiple recurring and overlapping definitions of analytics, named Business Visual Analytics (BVA). This is a new conceptual viewpoint mainly focused on the innovative data visualization techniques, business analytics capabilities and achieving business goals and performance. The paper is reporting research in progress and for this reason, further architectural developments and related validations are planned for further research.

Open government data initiatives result in the expectation of having open data available. Nevertheless, some potential risks like sensitivity, privacy, ownership, misinterpretation, and misuse of the data result in the reluctance of governments to open their data. At this moment, there is no comprehensive overview nor a model to understand the mechanisms resulting in risk when opening data. This study is aimed at developing a Bayesian-belief Networks (BbN) model to analyse the causal mechanism resulting in risks when opening data. An explanatory approach based on the four main steps is followed to develop a BbN. The model presents a better understanding of the causal relationship between data and risks and can help governments and other stakeholders in their decision to open data. We use the literature review base to quantify the probability of risk variables to give an illustration in the interrogating process. For the further study, we recommend using expert’s judgment for quantifying the probability of the risk variables in opening data.

Business applications often use such data structures as Presence Patterns for presentation of numbers of customers in service-oriented businesses including education, retailing and media. Presence Patterns contain open data derived from internal data of organizations. In this paper, we investigate different ways of defining Presence Patterns and possible privacy consequences dependent on the chosen definition. The first contribution of the paper is a definition of a family of Presence Patterns. The second contribution is a method for privacy analysis of Presence Patterns.

As a megatrend, the so-called Digital Transformation describes the entire potential of new technologies in economy. Thereby new approaches are needed to reveal this potential for companies. Looking at the literature a structured guideline is missing to reveal this potential by developing new digital business models (DBM) from the scratch. Therefore, this paper develops a structured framework to create ideas for DBMs. It contributes to business research by improving the initiation of digital reengineering. The resulting framework is called the Digitization Driven Design model (=D3 model). The application of this approach is described with a consulting-company use case. Initial evaluations show the first promising expert feedbacks and possibilities for future research.

Original Equipment Manufacturers are increasingly focusing on cooperation with a small number of risk-sharing partners who co-design and deliver key subsystems of the finished product. This trend increases collaboration activities throughout the supply chains involving suppliers of all sizes, including innovative small to medium-sized enterprises. The movement to Industry 4.0 concept such as “lot size of one” and demand-responsive production means these collaborations would be formed “on the fly” to respond to fast changing market needs and ever shorter product lifecycles.Research has created models and approaches claiming to provide effective software support for such collaborations on demand (also known as instant virtual enterprises); however, these have yet to be implemented and widely applied by suppliers and manufacturers. Research literature, whilst still praising the theoretical advantages and transformative nature of dynamic value chains, is starting to note (as yet undisclosed) economic, managerial and technological concerns that impede uptake of these ideas.In this paper we analyze exploratory interviews with a number of suppliers in the aerospace industry, and reveal key barriers such as lack of trust, switching costs, information asymmetry and path dependencies that prevent the uptake of short-term collaborations and present them in the sequence they appear forming supplier collaborations on demand.

The Industrial Internet of Things and Industry 4.0 both promote Smart Factory as the manufacturing of the future. The complexity of production environments consisting of smart entities is far more complex than traditional production scenarios. In particular SMEs have problems with the transition towards Smart Factory. Modelling could support this process. Available modelling frameworks are either too abstract for SMEs and their production networks or far too detailed to be suitable for discussing Smart Factory approaches within the companies and their production networks. A suitable modelling framework has been developed in a German research project on modelling the textile factory of the future, funded by the German Federal Ministry of Education and Research. The smart entity is the core element of the framework, which is detailed by five sub-models. This entity now can be used to describe machines, services as well as complete production networks with the same building blocks. Textile SMEs can engineer their structures towards Smart Factory with a modelling attuned to their needs. The capabilities of the modelling framework are demonstrated by their application to a carbon fibre nonwoven fabric production process.

Supply chains consist of a network of people, activities, resources and organizational systems that coordinate to move a product or services from one point to another. A typical supply chain network contains multiple different actors that have different needs and operate under different business conditions. The complex and lengthy structure of agri-food supply chains for instance makes it difficult for analysts to identify and model the appropriate business processes. Several process reference models have been provided for describing process models, but these tend to omit explicit guidance for configuring supply chain business processes particularly in the agri-food supply chains. Hence, this paper applies and adapt the SCOR model levels to demonstrate an approach for supporting configuration of supply chain business processes dedicated for supply chains. We illustrate the approach for an industrial case of a cocoa supply chain. The approach was also applied to define and model the level 4 processes which is out of scope of the SCOR model and scarce in literature for many sectors including our illustrated case.

The main purpose of this paper is to develop a model that assures the alignment between business and IT (BITA) for IT projects, based on the Ampersand method. BITA is essential in gaining value from IT investments to improve technical and human performance, to produce enhanced organizational strategies that yield competitive advantage and to perform better than businesses that do not align their business strategies with their IT strategies. The literature research proposes that Ampersand together with the Business Motivation Model (BMM) can assure BITA in two ways. First, it assures strategic fit between business strategy and business infrastructure and processes. Secondly, it assures functional integration between business infrastructure and processes, and IS infrastructure and processes. The BMM identifies ends (vision, goals, and objectives) and means to achieve the ends (missions, strategies, tactics, and business rules) of an organization. These concepts are relevant to an IT project, especially during the requirements engineering (RE) phase. For an organization to be effective and efficient, the ends and means have to be related to each other in some way. The proposed model records the relations between the ends and means and additionally checks the integrity of these relations using multiplicity constraints and business rules by using the Ampersand method.

In this paper, an expert system developed with an ontology-based approach to detect Service Level Agreement (SLA) violations is presented. The widespread use of SLAs in various areas complicates SLA management and in particular the detection of violations. Although it is necessary to automatically detect SLA violations, developing a different solution for each domain is quite costly. Several domains were investigated, and many common concepts have been identified in terms of SLAs. Nevertheless, it has been determined that each domain has its own distinct metrics and criteria. By combining familiar and changeable concepts, we have acquired the idea of creating a generic SLA ontology for different SLA domains. After generic SLA ontology was created, an expert system called SLAVIDES was developed using this ontology. The developed expert system is designed to detect SLA violations, check constraints, and make inferences. The developed system has been tested on the SLA data of the telecommunication domain. The results show that the proposed system can correctly detect SLA violations.

This paper combines the concept of multi-sided platforms and the emerging market of the Internet of Things (IoT). At present, the IoT market is in an early stage, with an abundance of fragmented solutions targeting specific domains and/or specific types of applications. In this paper, we defend our position that software back-end IoT platforms offer considerable potential to become leading multi-sided platforms as the core of the complex IoT ecosystem and hence, a race for domination of the IoT platform market will appoint these platform leaders. Furthermore, some important trade-offs are discussed for platform providing companies that aspire to become leading multi-sided platforms in the Internet of Things industry.

Currently, urban vehicle navigation is considered important because smoothness of traffic is needed for the sake of safety and cleanliness of air; in this regard, people essentially count on GPS navigation. Nevertheless, due to signal building shadows, GPS signal disturbances may occur in an urban environment; then alternative navigation options should be considered. It is therefore a matter of context-awareness to establish whether or not GPS navigation is available and adjust the delivered navigation services accordingly. In this paper we propose a top-down approach for modeling context-aware vehicle navigation systems; firstly, a high-level technology-independent conceptual model is derived (featuring context-awareness in general) and then this model is mapped towards corresponding lower-level technical solutions. Even though research materials, touching upon context-awareness or vehicle navigation, can be found in literature, we miss holistic approaches that align high-level (conceptual) goals with navigation-specific (technical) solutions; hence, our proposal is considered a step forward in this regard. We also propose an innovation concerning the technical aspects of navigation. Our proposed approach is partially validated by means of experiments.

Nowadays companies like Apple create ecosystems of third-party providers and users around their software platforms. Often online stores like Apple App Store are created to directly market third-party solutions. We call such ecosystems store-oriented software ecosystems. While the architecture of these ecosystems is mainly derived from business decisions of their owners, ecosystems with greatly different architectural designs have been created. This diversity makes it challenging for future ecosystem providers to understand which architectural design is suitable to fulfill certain business decisions. In turn, opening a platform becomes risky while endangering intellectual property or scarifying quality of services. In this paper, we identify three main design options of store-oriented software ecosystems by classifying existing ecosystems based on similarities in their business decisions. We elaborate on the design options, discuss their main contributions, and provide exemplary ecosystems. Our work provides aspiring ecosystem providers with the reusable knowledge of existing ecosystems and helps them to take more informed architectural decisions and reduce risks in future.

A business process is a structure of inter-related activities that are executed in order to achieve a specific business objective. Organizations often maintain multiple variants of a given business process because of changing conditions, different regulations in different countries, or other contextual factors. We aim at specifying the relationship between a generic business process and its different variants, taking the perspective of public values, such as privacy, accountability, and transparency. The business process variants in turn may be a basis for software specifications – in this, business processes would be bridging between societal demands (possibly concerning public values) and the corresponding technical (software) functionalities. Our contribution is featuring a meta-model that describes business processes on a value-independent level; they can be extended towards value-specific business process variants that can be related in turn to software architectures. We reflect this in proposed value operationalization guidelines, using concepts from business process design as a basis; those guidelines assume coming firstly through technology-independent artefacts and secondly – through technology-specific artefacts, to arrive at software specifications that are adequate with regard to public-values-related demands.

Public values are desires of the general public, that are about properties considered societally valuable, such as respecting the privacy of citizens or prohibiting polluting activities. “Translating” public values into functional solutions is thus an actual challenge. Even though Value-Sensitive Design (VSD) is about weaving public values in the design of (technical) systems, it stays insufficiently concrete as it concerns the alignment between abstract public values and technical (software) solutions. Still, VSD indirectly inspires ideas in that direction as for example the idea to consider business process variants for achieving such an alignment. Nevertheless, this is all about “atomic” public values (encapsulating only one particular behavioral goal) while one would often face public values that are “composite” in the sense that they reflect a particular human attitude rather than just a desired behavioral goal. In the current paper, we propose a value decomposition approach that allows for operationalizing composite public values. We also present experimental results featuring data analytics using self-administrated surveys.

Microservice Architecture is an innovative architectural style used for the design of software applications as independent, loosely coupled and strongly cohesive deployable services. Architecture Description Languages (ADLs) are formal languages used in the domain of Software Architecture in order to describe software intensive systems. In this paper, we introduce μσADL, a new ADL which is an extension on top of jADL (a formal ADL). μσADL aims to provide to software architects the means to adequately describe software systems that adopt this new architectural style. It provides an additional layer of abstraction omitting or hiding rigorous definitions. The basic concepts of μσADL are presented through an illustrative example. We propose a methodology for designing software systems that are built based upon this architectural style, using μσADL. Through an illustrative example, we present how from a Business Process Modelling Notation diagram, designers can obtain a verifiable formal specification of the architecture. Additionally, designers or programmers that follow this methodology can obtain source code stubs.

Software projects typically need to be monitored in detail regarding when what was done in order to demonstrate adherence to methodologies, rules, regulations, guidelines or best practices. To this end, it is of utmost importance to obtain factual knowledge from empirical evidence about the actual software development process. A major problem in this context is the lack of a centralized control of by a central system. Although it is hard to obtain full knowledge of the overall software development process, several cues can be gathered by analyzing pieces of information that are stored by supporting IT systems (e.g., issue trackers and version control). This position paper presents research in progress for extracting process knowledge from the historical data of software artifacts. This work extends the applicability of process mining techniques to software processes.

Software teams address software defect problems in a simple way: they identify them, assign them and resolve them. Nevertheless, studies have proven that having only these activities as approaches to handle a large and increasing number of software defects is inefficient. As a solution to this, we propose in this study a managerial conceptual tool for mining software defects in order to improve the management of SDs. With our proof of concept, we demonstrate how SDs mining management can be enhanced from a strategic and operational view. This is done through the precise definition of software defects’ management objectives in line with the objectives of the software product owner.