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For companies developing web-based applications, the Dev and the Ops refer to different groups with either operational or development focus. Therefore, DevOps help these companies streamline software development and operations activities by emphasizing the collaboration between the two groups. However, for companies producing software-intensive products, the Ops would refer to customers who use and operate the product. In addition, companies producing software-intensive products do not only offer products to customers but rather Product Service Systems (PSS), where product-related services play a key role in ensuring customer satisfaction besides their significant revenue contribution. Thus, the context of product-oriented PSS is very different from web-based applications, making it difficult to apply DevOps without considering the role of the services. Therefore, based on a two years participant observation case study conducted at a multinational telecommunications systems provider, we propose a new and novel approach called Development-Services-Operations (DevServOps) which incorporates services as a key player facilitating an end-to-end software flow toward customers in one direction and feedback toward developers in the other direction. Services become the glue that connects the Dev and the Ops, achieved by providing internal services to increase the precision of the development organization and external services to increase the speed of deployment and new content adoption on the customers' side.

With the digitalization of industry and society, product management of digital technologies such as software, data and AI is rapidly becoming critically important. Digitalization offers new tools for product managers to increase the return on investment of R&D including DevOps, DataOps and MLOps. These fast feedback loops allow product managers to adopt a much more experiment- and data-driven approach, e.g. using A/B testing and other experimental approaches. Product management is at the transition point from “what to build” to “what outcomes to accomplish”. The keynote addresses this transformation, the challenges that one has to address while transitioning, the benefits as well as the new techniques available. The talk will share numerous examples from industry based on our research in the context of Software Center (www.software-center.se), a collaboration between 15 international companies and 5 Swedish universities focused on accelerating the digital transformation of the European software intensive industry.

For decades, product data has been collected and used for quality assurance, for post-deployment defect detection and for informing the next generation of products. Across industry domains, and with the online domain leading the way, companies have adopted experimentation and data driven practices such as A/B testing to evaluate product performance, customer behaviors and for determining what adds value to customers. However, with the rapid changes that new digital technologies bring, companies are moving towards continuous value delivery and monetization models in which they offer their products as-a-service or offer services to complement and extend their existing products. In this transition, the traditional way of post-deployment data collection and use is no longer sufficient. While companies realize this, they experience difficulties in making the changes they need to transition towards continuous practices and new ways of working with data. As a result, companies risk wasting development efforts on functionality that have little or no customer value and they lose out on the competitive advantages that come with insights derived from continuous collection and use of data. In this paper, we explore the challenges companies experience in the transition from traditional towards continuous practices and the implications this shift has on their ways of working with data.

Direct transportation to a thrombectomy-capable intervention center is beneficial for patients with ischemic stroke due to large vessel occlusion (LVO), but can delay intravenous thrombolytics (IVT). The aim of this modeling study was to estimate the effect of prehospital triage strategies on treatment delays and overtriage in different regions. We used data from two prospective cohort studies in the Netherlands: the Leiden Prehospital Stroke Study and the PRESTO study. We included stroke code patients within 6 h from symptom onset. We modeled outcomes of Rapid Arterial oCclusion Evaluation (RACE) scale triage and triage with a personalized decision tool, using drip-and-ship as reference. Main outcomes were overtriage (stroke code patients incorrectly triaged to an intervention center), reduced delay to endovascular thrombectomy (EVT), and delay to IVT. We included 1798 stroke code patients from four ambulance regions. Per region, overtriage ranged from 1-13% (RACE triage) and 3-15% (personalized tool). Reduction of delay to EVT varied by region between 24 ± 5 min (n = 6) to 78 ± 3 (n = 2), while IVT delay increased with 5 (n = 5) to 15 min (n = 21) for non-LVO patients. The personalized tool reduced delay to EVT for more patients (25 ± 4 min [n = 8] to 49 ± 13 [n = 5]), while delaying IVT with 3-14 min (8-24 patients). In region C, most EVT patients were treated faster (reduction of delay to EVT 31 ± 6 min (n = 35), with RACE triage and the personalized tool. In this modeling study, we showed that prehospital triage reduced time to EVT without disproportionate IVT delay, compared to a drip-and-ship strategy. The effect of triage strategies and the associated overtriage varied between regions. Implementation of prehospital triage should therefore be considered on a regional level.

Data has been claimed to be the new oil of the 21st century as it has seen to be able both to improve the existing products and services as well as to create new revenue streams for its utilizing company with a secondary customers base. However, while there is active streams of research for developing machine learning and data science methods, considerably less has been done to understand and characterize data business activities in the software-intensive companies. This study uses a multiple case study approach in the software-intensive embedded system domain. Four large international embedded system companies were selected as the case study subjects. The objective is to understand how the case companies are developing their activities for successful utilization of the data. The study identifies six distinct stages with their own challenges. In addition, this study serves as a starting for further work for supporting software-intensive embedded system companies to start data business. Post-print / Final draft

Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating software changes. In the automotive domain, running randomised field experiments is not always desired, possible, or even ethical. In the face of such limitations, we develop a framework BOAT (Bayesian causal modelling for ObvservAtional Testing), utilising observational studies in combination with Bayesian causal inference, in order to understand real-world impacts from complex automotive software updates and help software development organisations arrive at causal conclusions. In this study, we present three causal inference models in the Bayesian framework and their corresponding cases to address three commonly experienced challenges of software evaluation in the automotive domain. We develop the BOAT framework with our industry collaborator, and demonstrate the potential of causal inference by conducting empirical studies on a large fleet of vehicles. Moreover, we relate the causal assumption theories to their implications in practise, aiming to provide a comprehensive guide on how to apply the causal models in automotive software engineering. We apply Bayesian propensity score matching for producing balanced control and treatment groups when we do not have access to the entire user base, Bayesian regression discontinuity design for identifying covariate dependent treatment assignments and the local treatment effect, and Bayesian difference-in-differences for causal inference of treatment effect overtime and implicitly control unobserved confounding factors. Each one of the demonstrative case has its grounds in practise, and is a scenario experienced when randomisation is not feasible. With the BOAT framework, we enable online software evaluation in the automotive domain without the need of a fully randomised experiment., Comment: In submission

Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless communications. Due to unique characteristics such as mobility, flexible deployment, and rapid reconfiguration, drones can readily change location dynamically to provide on-demand communications to users on the ground in emergency scenarios. As a result, the usage of UAV base stations (UAV-BSs) has been considered an appropriate approach for providing rapid connection in MC scenarios. In this paper, we study how to control multiple UAV-BSs in both static and dynamic environments. We use a system-level simulator to model an MC scenario in which a macro BS of a cellular network is out of service and multiple UAV-BSs are deployed using integrated access and backhaul (IAB) technology to provide coverage for users in the disaster area. With the data collected from the system-level simulation, a deep reinforcement learning algorithm is developed to jointly optimize the three-dimensional placement of these multiple UAV-BSs, which adapt their 3-D locations to the on-ground user movement. The evaluation results show that the proposed algorithm can support the autonomous navigation of the UAV-BSs to meet the MC service requirements in terms of user throughput and drop rate.

Benchmark suites, i.e. a collection of benchmark functions, are widely used in the comparison of black-box optimization algorithms. Over the years, research has identified many desired qualities for benchmark suites, such as diverse topology, different difficulties, scalability, representativeness of real-world problems among others. However, while the topology characteristics have been subjected to previous studies, there is no study that has statistically evaluated the difficulty level of benchmark functions, how well they discriminate optimization algorithms and how suitable is a benchmark suite for algorithm comparison. In this paper, we propose the use of an item response theory (IRT) model, the Bayesian two-parameter logistic model for multiple attempts, to statistically evaluate these aspects with respect to the empirical success rate of algorithms. With this model, we can assess the difficulty level of each benchmark, how well they discriminate different algorithms, the ability score of an algorithm, and how much information the benchmark suite adds in the estimation of the ability scores. We demonstrate the use of this model in two well-known benchmark suites, the Black-Box Optimization Benchmark (BBOB) for continuous optimization and the Pseudo Boolean Optimization (PBO) for discrete optimization. We found that most benchmark functions of BBOB suite have high difficulty levels (compared to the optimization algorithms) and low discrimination. For the PBO, most functions have good discrimination parameters but are often considered too easy. We discuss potential uses of IRT in benchmarking, including its use to improve the design of benchmark suites, to measure multiple aspects of the algorithms, and to design adaptive suites., In submission

Purpose Between 0.1—3% of injured children who present at a hospital emergency department ultimately die as a result of their injuries. These events are typically reported as unnatural causes of death and may result from either accidental or non-accidental trauma (NAT). Examples of the latter include trauma that is inflicted directly or resulting from neglect. Although consultation with a forensic physician is mandatory for all deceased children, the prevalence of fatal inflicted trauma or neglect among children is currently unclear. Methods This is a retrospective study that included children (0–18 years) who presented and died at one of the 11 Level I trauma centers in the Netherlands between January 1, 2014, and January 1, 2019. Outcomes were classified based on the conclusions of the Child Abuse and Neglect team or those of forensic pathologists and/or the court in cases referred for legally mandated autopsies. Cases in which conclusions were unavailable and there was no clear accidental cause of death were reviewed by an expert panel. Results The study included 175 cases of childhood death. Seventeen (9.7%) of these children died due to inflicted trauma (9.7%), 18 (10.3%) due to neglect, and 140 (80%) due to accidents. Preschool children (versus 6%, p Conclusion One of every five pediatric deaths in our nationwide Level I trauma center study was attributed to NAT; 44% of these deaths were the result of trauma experienced by preschool-aged children. A remarkable number of fatal drownings were due to neglect. Postmortem radiological studies and autopsies were performed in only one-fifth of all deceased children. The limited use of postmortem investigations may have resulted in missed cases of NAT, which will result in an overall underestimation of fatal NAT experienced by children.

Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised field experiments is not always desired, possible or even ethical in the development of automotive embedded software. In the face of such restrictions, we propose the use of the Bayesian propensity score matching technique for causal inference of observational studies in the automotive domain. In this paper, we present a method based on the Bayesian propensity score matching framework, applied in the unique setting of automotive software engineering. This method is used to generate balanced control and treatment groups from an observational online evaluation and estimate causal treatment effects from the software changes, even with limited samples in the treatment group. We exemplify the method with a proof-of-concept in the automotive domain. In the example, we have a larger control ($N_c=1100$) fleet of cars using the current software and a small treatment fleet ($N_t=38$), in which we introduce a new software variant. We demonstrate a scenario that shipping of a new software to all users is restricted, as a result, a fully randomised experiment could not be conducted. Therefore, we utilised the Bayesian propensity score matching method with 14 observed covariates as inputs. The results show more balanced groups, suitable for estimating causal treatment effects from the collected observational data. We describe the method in detail and share our configuration. Furthermore, we discuss how can such a method be used for online evaluation of new software utilising small groups of samples., Comment: To appear at the 28th Asia-Pacific Software Engineering Conference (APSEC 2021)

Heterogeneous computing is one of the most important computational solutions to meet rapidly increasing demands on system performance. It typically allows the main flow of applications to be executed on a CPU while the most computationally intensive tasks are assigned to one or more accelerators, such as GPUs and FPGAs. The refactoring of systems for execution on such platforms is highly desired but also difficult to perform, mainly due the inherent increase in software complexity. After exploration, we have identified a current need for a systematic approach that supports engineers in the refactoring process -- from CPU-centric applications to software that is executed on heterogeneous platforms. In this paper, we introduce a decision framework that assists engineers in the task of refactoring software to incorporate heterogeneous platforms. It covers the software engineering lifecycle through five steps, consisting of questions to be answered in order to successfully address aspects that are relevant for the refactoring procedure. We evaluate the feasibility of the framework in two ways. First, we capture the practitioner's impressions, concerns and suggestions through a questionnaire. Then, we conduct a case study showing the step-by-step application of the framework using a computer vision application in the automotive domain., Manuscript submitted to the Journal of Systems and Software

System logs perform a critical function in software-intensive systems as logs record the state of the system and significant events in the system at important points in time. Unfortunately, log entries are typically created in an ad-hoc, unstructured and uncoordinated fashion, limiting their usefulness for analytics and machine learning. In this paper, we present the main challenges of contemporary approaches to generating and storing system logs data for large, complex, software-intensive systems based on an in-depth case study at a world-leading telecommunications company. Second, we present a systematic and structured approach for generating log data that does not suffer from the aforementioned challenges and is optimized for use in machine learning. Third, we provide validation of the approach based on expert interviews that confirms that the approach addresses the identified challenges and problems.

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this paper, we provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that we have studied. The main contribution of the paper is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large., Comment: 8 pages, 4 figures

Hintergrund: Die Hereditäre hämorrhagische Teleangiektasie (HHT) ist eine systemische autosomal dominant vererbbare Erkrankung. Die Diagnose wird entweder klinisch anhand der Curaçao-Kriterien oder anhand einer genetischen Untersuchung gestellt. Organmanifestationen betreffen insbesondere[zum vollständigen Text gelangen Sie über die oben angegebene URL], Jahrestagung der Vereinigung Westdeutscher Hals-Nasen-Ohren-Ärzte

Einleitung: Aufgrund rezidivierender Epistaxis und damit einhergehendem Kontrollverlust ist die Lebensqualität der Patienten mit hereditärer hämorrhagischer Teleangiektasie (HHT) deutlich gemindert. Insbesondere die Länge der Epistaxis schränkt die Patienten in ihrem alltäglichen[zum vollständigen Text gelangen Sie über die oben angegebene URL], Jahrestagung der Vereinigung Westdeutscher Hals-Nasen-Ohren-Ärzte

peer-reviewed The rapid pace with which software needs to be built, together with the increasing need to evaluate changes for end users both quantitatively and qualitatively calls for novel software engineering approaches that focus on short release cycles, continuous deployment and delivery, experiment-driven feature development, feedback from users, and rapid tool-assisted feedback to developers. To realize these approaches there is a need for research and innovation with respect to automation and tooling, and furthermore for research into the organizational changes that support flexible data-driven decision-making in the development lifecycle. Most importantly, deep synergies are needed between software engineers, managers, and data scientists. This paper reports on the results of the joint 5th International Workshop on Rapid Continuous Software Engineering (RCoSE 2019) and the 1st International Workshop on Data-Driven Decisions, Experimentation and Evolution (DDrEE 2019), which focuses on the challenges and potential solutions in the area of continuous data-driven software engineering.

Collaborative modeling is gaining a growing interest in both academia and industry. However, several research challenges remain open, including scalability, support for multi-user modeling environments, model versioning, migration, comparison, merging and conflict management. The workshop aims at assessing the state of the art and practice on Collaborative MDE, creating new synergies between tool vendors, researchers, and practitioners, informing the community about the new means for collaborative MDE, and identifying needs and research gaps in the collaborative MDE area. COMMitMDE 2018 brought together researchers and practitioners to explore (i) the impact of collaborative SE methods and principles on MDE practices and (ii) how MDE methods and techniques can support collaborative software engineering activities.

Model-based languages such as MATLAB/Simulink play an essential role in the model-driven development of software systems. To comply with new requirements, it is common practice to create new variants by copying existing systems and modifying them. Commonly referred to as clone-and-own, severe problems arise in the long-run when no dedicated variability management is installed. To allow for a documented and structured reuse of systems, their variability information needs to be reverse-engineered. In this paper, we propose an advanced comparison procedure, the Matching Window Technique, and a customizable metric. Both allow us to overcome structural alterations commonly performed during clone-and-own. We analyze related MATLAB/Simulink models and determine, classify and represent their variability information in an understandable way. With our technique, we assist model engineers in maintaining and evolving existing variants. We provide three feasibility studies with real-world models from the automotive domain and show our technique to be fast and precise. Furthermore, we perform semi-structured interviews with domain experts to assess the potential applicability of our technique in practice.

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

BACKGROUND: In patients with a symptomatic abdominal aortic aneurysm (sAAA), acute intervention theoretically reduces rupture risk prior to surgery whereas delayed intervention provides surgery under optimised conditions. In the present study we evaluated differences in 30-day mortality in patients with a sAAA operated within 12 hours compared to patients who received treatment after 12 hours and who were optimized for surgery.METHODS: All patients with a sAAA who were treated within one week after presentation were included in the analyses. The 30-day mortality rates of patients operated within 12 hours were compared to those operated after 12 hours, adjusted for type of operation and for all potential confounders.RESULTS: Of the 89 included patients, 37 patients received surgery within 12 hours. In patients treated within 12 hours, 30-day mortality rate was 6(16.2%) compared to 3 (5.8%) in patients treated after 12 hours (odds ratio 0.316; CI 0.074-1.358). When adjusted for type of operation and other confounders, odds ratios were 0.305 (CI 0.066-1.405) and 0.270 (CI 0.015-4.836), respectively.CONCLUSIONS: In a substantial amount of patients with an alleged symptomatic AAA, delayed surgery with patient optimisation might be justified. However, specific criteria in order to select patients that might benefit from delayed surgery need further investigation.

This report documents the program and the outcomes of Dagstuhl Seminar 18182 "Software Business, Platforms, and Ecosystems: Fundamentals of Software Production Research".

Since its rise to general awareness and popularity starting close to two decades ago, the concept of software product lines has taken the center stage in the software reuse community. After more than four decades of research into effective and efficient reuse of software inside the four walls of the organization, and countless initiatives, software product lines presented an approach that has proven to provide real productivity improvements in the development cost of software intensive products. This has allowed companies to increase their product portfolio with an order of magnitude, to allow for much higher degrees of configurability by customers, facilitated common look-and-feel across a wide product population and enabled companies to be more innovative by decreasing the cost of new product experiments. It achieved this by broadening the scope of study from technology to include process, business strategy and organizational aspects. Successful product lines address all aspects relevant to the organization and then adopt and institutionalize the approach in the company. This special issue features eight papers from the 14th Software Product Line Conference (SPLC 2010). SPLC provides an institution and the premier meeting place for the software product line community. In particular, SPLC 2010 was held in Jeju, South Korea, and the accepted papers covered various areas of software product line engineering including product line contexts, variability management, formal approaches, product validation, and feature modeling. We invited eight top quality papers and they were significantly improved and extended for this special issue.

Real-time programs are, in general, difficult to design and verify. The inheritance mechanism can be useful in reusing well-defined and verified real-time programs. In applications developed by current real-time object-oriented languages, however, changing application requirements or changing real-time specifications in sub-classes may require excessive redefinitions although this seems to be intuitively unnecessary. We refer to this as the real-time specification inheritance anomaly. This paper introduces three kinds of real-time specification inheritance anomalies that one may experience while constructing object-oriented programs. As a solution to these anomalies, the concept of real-time composition filters is introduced. Filters affect the real-time characteristics of messages that are received or sent by an object. Through proper configuration of filters, one can specify real-time constraints, and reuse of these constraints without causing inheritance anomalies.

Currently, two treatment options for ruptured abdominal aortic aneurysms (AAA) are available: conventional open surgical repair and endovascular minimally invasive repair. Open repair carries a significant morbidity and mortality rate, most probably due to the combined effects of general anaesthesia and surgical exposure. Based on anatomical characteristics as assessed by CT scan, approximately half of the patients with a ruptured AAA are eligible for endovascular minimally invasive repair. The majority of comparative studies show a clear trend towards a lower perioperative mortality rate for endovascular repair compared to open surgery (18-24% and 34-38%, respectively) and more rapid recovery. However, these mainly observational studies show considerable heterogeneity. Furthermore, potential selection bias, where patients with a more favourable anatomic and haemodynamic profile are more likely to be selected for endovascular repair, makes a proper comparison barely possible.The potential benefits of endovascular repair over open surgery are currently being evaluated in a large randomised study

Software architectures have high costs for change, are complex, and erode during evolution. We believe these problems are partially due to knowledge vaporization. Currently, almost all the knowledge and information about the design decisions the architecture is based on are implicitly embedded in the architecture, but lack a first-class representation. Consequently, knowledge about these design decisions disappears into the architecture, which leads to the aforementioned problems. In this paper, anew perspective on software architecture is presented, which views software architecture as a composition of a set of explicit design decisions. This perspective makes architectural design decisions an explicit part of a software architecture. Consequently, knowledge vaporization is reduced, thereby alleviating some of the fundamental problems of software architecture.

Many variability modeling approaches consider only formalized dependencies, i.e. in- or exclude relations between variants. However, in real industrial product families, dependencies are often much more complicated. In this paper, we discuss the product derivation problems associated with dependencies, and show how our variability modeling framework COVAMOF addresses these issues. Throughout the paper, we use examples of Intrada, an intelligent traffic systems family of Dacolian B.V.

Product-line architectures, i.e. a software architecture and component set shared by a family of products, represents a promising approach to achieving reuse of software. Several companies are initiating or have recently adopted a product-line architecture. However, little experience is available with respect to the evolution of the products, the software components and the software architecture. Due to the higher level of interdependency between the various software assets, software evolution is a more complex process. In this paper, we discuss the results of two case studies concentrating on the evolution of software assets in two swedish organizations that have employed the product-line architecture approach for several years. Based on these two cases, we discuss the commonalities, presented as categorizations of the evolution of the requirements, the software architecture and the software components, and also the differences between the two cases. Published in an edited form as: Mikael Svahnberg, Jan Bosch, Evolution in Software Product Lines: Two Cases, in Journal of Software Maintenance - Research and Practice 11(6), pp. 391-422, 1999.

The software architecture plays an important role in achieving a modifiable system, but few methods for modifiability analysis exist. In this paper, we propose an analysis method for software architecture modifiability that has successfully been applied in several cases. The method consists of five main steps, i.e. goal selection, software architecture description, scenario elicitation, scenario evaluation and interpretation.

Over the last decade, several authors have studied the maintainability of software architectures. In particular, the assessment of maintainability has received attention. However, even when one has a quantitative assessment of the maintainability of a software architecture, one still does not have any indication of the optimality of the software architecture with respect to this quality attribute. Typically the software architect is supposed to judge the assessment result based on his or her personal experience. In this paper we propose a technique for analysing the optimal maintainability of a software architecture based on a specified scenario profile. This technique allows software architects to analyse the maintainability of their software architecture with respect to the optimal maintainability. The technique is illustrated and evaluated using industrial cases.

Modifiability is an important quality for software systems, because a large part of the costs associated with these systems is spent on modifications. The effort, and therefore cost, that is required for these modifications is largely determined by a system’s software architecture. Analysis of software architectures is therefore an important technique to achieve modifiability and reduce maintenance costs. However, few techniques for software architecture analysis currently exist. Based on our experiences with software architecture analysis of modifiability, we have developed an analysis method consisting of five steps. In this paper we report on our experiences with this method. We illustrate our experiences with examples from two case studies of SAA of modifiability. These case studies concern a system for mobile positioning at Ericsson Software Techology and a system for freight handling at DFDS Fraktarna. Our experiences are related to each step of the analysis process. In addition, we made some observations on SAA of modifiability in general. Beskriver erfarenheterna från två fall studier. submitted for publication at HICSS conference.

Object-oriented application frameworks present one of the most successful approaches to developing reusable assets in industry, but developing frameworks is both difficult and expensive. Framework generally evolve to maturity through a number of iterations due to the incorporation of new requirements and better domain understanding. Since changes to frameworks have a large impact due to the effects on the applications build based on the asset, it is important to assess the maturity of a framework. Bansiya [3, 4] presents an approach to assessing framework maturity based on a set of design metrics and formulates four statements. In this paper, we present the results of a replicated case study of the framework maturity assessment approach. Our study subject consists of four successive versions of a proprietary black-box application framework. Our findings partly support the statements formulated in the original study, but differ in some places. The differences are discussed and explanations and argumentation provided.

Scenario profiles are used increasingly often for the assessment of quality attributes during the architectural design of software systems. However, the definition of scenario profiles is subjective and no data is available on the effects of individuals on scenario profiles. In this paper we present the design, analysis and results of a controlled experiment on the effect of individuals on scenario profiles, so that others can replicate the experiments on other projects and people. Both scenario profiles created by individuals and by groups are studied. The findings from the experiment showed that groups with prepared members proved to be the best method for creating scenario profiles. Unprepared groups did not perform better than individuals when creating scenario profiles.

Product-line architectures present an important approach to increasing software reuse and reducing development cost by sharing an architecture and set of reusable components among a family of products. However, evolution in product-line architectures is more complex than in traditional software development since new, possibly conflicting, requirements originate from the existing products in the product-line and new products that are to be incorporated. In this paper, we present a case study of product-line architecture evolution. Based on the case study, we develop categorizations for the evolution of requirements, the product-line architecture and product-line architecture components. Subsequently, we analyze and present the relations between these categorizations.

Product-line architectures present an important approach to increasing software reuse and reducing development cost by sharing an architecture and set of reusable components among a family of products. However, evolution in product-line architectures is more complex than in traditional software development since new, possibly conflicting, requirements originate from the existing products in the product-line and new products that are to be incorporated. In this paper, we present a case study of product-line architecture evolution. Based on the case study, we develop categorizations for the evolution of requirements, the product-line architecture and product-line architecture components. Subsequently, we analyze and present the relations between these categorizations.

Object-oriented framework technology has become a common reuse technology in object-oriented software development. As with all software, frameworks tend to evolve. Once the framework has been deployed, new versions of a framework cause high maintenance cost for the products built with the framework. This fact in combination with the high costs of developing and evolving an object-oriented framework make it important to have controlled and predictable evolution of the framework?s functionality and costs. We present three methods 1) Evolution Identification Using Historical Information, 2) Stability Assessment and 3) Distribution of Development Effort which have been applied to between one to three different frameworks, both in the proprietary and commercial domain. The methods provide management with information which will make it possible to make well-informed decisions about the framework?s evolution, especially with respect to the following issues; identification of evolution-prone modules, framework deployment, change impact analysis, benchmarking and requirements management. Finally, the methods are compared to each other with respect to costs and benefits.

In method-based software development, software engineers create artifacts based on the heuristic rules of the adopted method. Most CASE tools, however, do not actively assist software engineers in applying the heuristic rules. To provide an active support, the rules must be formalized, implemented and integrated within the framework of current CASE tools. In this paper we describe an approach for formalizing the heuristic rules of methods.

In the last several years, a considerable number of object-oriented methods have been introduced to create robust, reusable and adaptable software systems [1], [2], [3], [4]. Object-oriented methods define a considerable number of rules which are generally expressed by using two-valued logic. For instance, an entity in a requirement specification is either accepted or rejected as a class. We consider two major problems in the way how rules are defined and applied in current object-oriented methods. The first problem, termed quantization problem, is a natural result of the incapacity of two-valued logic to express the approximate and inexact nature of a typical software development process. The second problem, termed contextual bias problem, arises because most of methods are not able to model the effects of the context on the validity of the method. To reduce these problems, we propose a new fuzzy logic-based object-oriented software development technique. This technique is not specific to a particular object-oriented method, but can be used to evaluate and enhance current methods. In addition, the application of fuzzy logic-based reasoning opens new perspectives to software development, such as fuzzy artifacts and accumulative software life-cycle.

Software engineers may experience problems in modeling certain aspects while applying object-oriented techniques [4, 10, 11]. Composition-Filters are capable of expressing various different kinds of aspects in a uniform manner. These aspects are, for example, inheritance and delegation [1] and atomic delegation [2], multiple views, dynamic inheritance and queries on objects [3], coordinated behavior and inter-object constraints [5], real-time [6] and composing real-time and synchronization together [9], synchronization [8] and distributed synchronization [7], and client-server architectures [10].

There are many similarities between industrial goods manufacturing and software development processes. This paper first briefly analyzes the recent developments in goods manufacturing, and then identifies the equivalent techniques in software technology. It is claimed that products developed during software manufacturing must be modeled as active artifacts. As a possible approach in this direction, an object-oriented artifact production framework is presented and evaluated.

The workshop on Modeling Software Processes and Artifacts explored the application of object technology in process modeling. After the introduction and the invited lecture, a number of participants presented their position papers. First, an overview is given on some background work, and the aims, aspects and approaches in software process modeling, and then we shortly comment on the position papers in this reader.

Reuse of software has been one of the main goals of software engineering for decades. Reusing software is not simple and most efforts resulted in small reusable, black-box components. With the emergence of the object-oriented paradigm, the enabling technology for reuse of larger components became available and resulted in the definition of object-oriented frameworks. Frameworks attracted attention from many researchers and software engineers and frameworks have been defined for a large variety of domains. The claimed advantages of frameworks are, among others, increased reusability and reduced time to market for applications. Although several examples have shown these advantages to exist, there are problems and hindrances associated with frameworks that may not appear before their usage in real projects. The authors have been involved in the design, maintenance and usage of several object-oriented frameworks and based on the experiences from these projects, a number of problems related to frameworks are descr ibed. The problems are organised according to four categories, i.e. framework development, usage, composition and maintenance. For each category, the most relevant problems and experiences are presented. This paper may help software engineers to avoid the described problems, whereas researchers may try to address these topics in their research.

The conventional object-oriented paradigm provides client-server message passing as the primary means of interaction between objects. Although this is appropriate in situations where one objects requests a service from another object, there are other situations where one object, the observer, depends on state-changes or actions occurring at another object, the target. In those situations, the object-oriented paradigm requires the target object to notify the observer objects, despite the fact that the target object does not benefit from the interaction. This inverted interaction scheme leads to problems such as increased coupling and decreased understandability, maintainability and reusability of the resulting classes. In the analysis of object interactions one can identify four roles: the benefitor, the establisher, the sender and the receiver. The aforementioned problems result from the fact that in client-server interaction, the benefitor and the sender are not always the same object. To address this, we pr opose the notion of causal connections, an alternative interaction mechanism complementing the traditional client-server interaction. Two implementations of causal connections are described; one in the context of C++ and another using the layered object model. It is shown that causal connections solve the problems mentioned above.

Measurement systems are of increasing importance for manufacturing, due to high automation level of production processes. Although most measurement systems have much in common and are expensive to construct, these systems are often developed from scratch, hardly reusing the available designs and implementations. To address this, we have designed and implemented an object-oriented framework for the domain of measurement systems that can be used as the core of measurement systems. Evaluations of the framework show that it captures the main concepts in the domain and that the required extensions for individual applications are limited. In this paper, a number of example framework instantiations are presented. The lessons we learned during the framework design and an evaluation of the object-oriented modelling paradigm are presented.