Modern software systems are increasingly more connected, pervasive, and dynamic, as such, they are subject to more runtime variations than legacy systems. Runtime variations affect system properties, such as performance and availability. The variations are difficult to anticipate and thus mitigate in the system design.

Self-adaptive software systems were proposed as a solution to monitor and adapt systems in response to runtime variations. Research has established a vast body of knowledge on engineering self-adaptive systems. However, there is a lack of systematic process support that leverages such engineering knowledge and provides for systematic reuse for self-adaptive systems development. 

This thesis proposes the Autonomic Software Product Lines (ASPL), which is a strategy for developing self-adaptive software systems with systematic reuse. The strategy exploits the separation of a managed and a managing subsystem and describes three steps that transform and integrate a domain-independent managing system platform into a domain-specific software product line for self-adaptive software systems.

Applying the ASPL strategy is however not straightforward as it involves challenges related to variability and uncertainty. We analyzed variability and uncertainty to understand their causes and effects. Based on the results, we developed the Autonomic Software Product Lines engineering (ASPLe) methodology, which provides process support for the ASPL strategy. The ASPLe has three processes, 1) ASPL Domain Engineering, 2) Specialization and 3) Integration. Each process maps to one of the steps in the ASPL strategy and defines roles, work-products, activities, and workflows for requirements, design, implementation, and testing. The focus of this thesis is on requirements and design.

We validate the ASPLe through demonstration and evaluation. We developed three demonstrator product lines using the ASPLe. We also conducted an extensive case study to evaluate key design activities in the ASPLe with experiments, questionnaires, and interviews. The results show a statistically significant increase in quality and reuse levels for self-adaptive software systems designed using the ASPLe compared to current engineering practices.

Software quality is critical in today's software systems. A challenge is the trade-off situation architects face in the design process. Designers often have two or more alternatives, which must be compared and put into context before a decision is made. The challenge becomes even more complex for dynamic software product lines, where domain designers have to take runtime variations into consideration as well. To address the problem we propose extensions to an architectural reasoning framework with constructs/artifacts to define and model a domain's scope and dynamic variability. The extended reasoning framework encapsulates knowledge to understand and reason about domain quality behavior and self-adaptation as a primary variability mechanism. The framework is demonstrated for a self-configuration property, self-upgradability on an educational product-line.

Advances in computing technologies are pushing software systems and their operating environments to become more dynamic and complex. The growing complexity of software systems coupled with uncertainties induced by runtime variations leads to challenges in software analysis and design. Self-Adaptive Software Systems (SASS) have been proposed as a solution to address design time complexity and uncertainty by adapting software systems at runtime. A vast body of knowledge on engineering self-adaptive software systems has been established. However, to the best of our knowledge, no or little work has considered systematic reuse of this knowledge. To that end, this study contributes an Autonomic Software Product Lines engineering (ASPLe) methodology. The ASPLe is based on a multi-product lines strategy which leverages systematic reuse through separation of application and adaptation logic. It provides developers with repeatable process support to design and develop self-adaptive software systems with reuse across several application domains. The methodology is composed of three core processes, and each process is organized for requirements, design, implementation, and testing activities. To exemplify and demonstrate the use of the ASPLe methodology, three application domains are used as running examples throughout the report.

Designing a software architecture requires architectural reasoning, i.e., activities that translate requirements to an architecture solution. Architectural reasoning is particularly challenging in the design of product-lines of self-adaptive systems, which involve variability both at development time and runtime. In previous work we developed an extended Architectural Reasoning Framework (eARF) to address this challenge. However, evaluation of the eARF showed that the framework lacked support for rigorous reasoning, ensuring that the design complies to the requirements. In this paper, we introduce an analytical framework that enhances eARF with such support. The framework defines a set of artifacts and a series of activities. Artifacts include templates to specify domain quality attribute scenarios, concrete models, and properties. The activities support architects with transforming requirement scenarios to architecture models that comply to required properties. Our focus in this paper is on architectural reasoning support for a single product instance. We illustrate the benefits of the approach by applying it to an example client-server system, and outline challenges for future work. © 2016 IEEE.

This report describes a work in progress to develop Autonomic Software Product Lines (ASPL). The ASPL is a dynamic software product line approach with a novel variability handling mechanism that enables traditional software product lines to adapt themselves at runtime in response to changes in their context, requirements and business goals. The ASPL variability mechanism is composed of three key activities: 1) context-profiling, 2) context-aware composition, and 3) online learning. Context-profiling is an offline activity that prepares a knowledge base for context-aware composition. The context-aware composition uses the knowledge base to derive a new product or adapts an existing product based on a product line's context attributes and goals. The online learning optimizes the knowledge base to remove errors and suboptimal information and to incorporate new knowledge. The three activities together form a simple yet powerful variability handling mechanism that learns and adapts a system at runtime in response to changes in system context and goals. We evaluated the ASPL variability mechanism on three small-scale software product lines and got promising results. The ASPL approach is, however, is yet at an initial stage and require improved development support with more rigorous evaluation. 

More than two decades of research have demonstrated an increasing need for software systems to be self-adaptive. Self-adaptation is required to deal with runtime dynamics which are difficult to predict before deployment. A vast body of knowledge to develop Self-Adaptive Software Systems (SASS) has been established. We, however, discovered a lack of process support to develop self-adaptive systems with reuse. To that end, we propose a domain-engineering based methodology, Autonomic Software Product Lines engineering (ASPLe), which provides step-by-step guidelines for developing families of SASS with systematic reuse. The evaluation results from a case study show positive effects on quality and reuse for self-adaptive systems designed using the ASPLe compared to state-of-the-art engineering practices.

Food waste is becoming an increasing threat to the environment and the economy. Estimates indicate that annually, a third of the food produced around the world ends up being wasted. Only one-fourth of that food is enough to take nearly a billion people out of starvation. Food waste is especially higher in more developed countries, including most of the states in the European Union and the USA. Sector-wise, food is being lost from field to fork, with households topping the charts. Overbuying, not knowing what already is in the fridge, unaware of the food until it eventually expires, are among the most common reasons that contribute to the food waste. The potential prevention of such massive waste could significantly reduce the amount of greenhouse gas emissions around the world and help the economy of the households including all the parties involved in food production, distributing and retailing.

On the other hand, technology has progressed in very rapid steps. The advancement of AI, ML, IoT, and voice-enabled devices has revolutionized many industries and has made us more efficient as human beings. Unfortunately, these advancements haven't yet had any significant impact in assisting families with their food choices and in preventing them from overbuying and throwing food away. Most of the proposed solutions addressing this issue, do not get integrated into everyday life. That is because they require a lot of manual input, rely entirely on mobile phones, do not show immediate results to keep users motivated, and on top of all, for the sole fact that modern lives are quite complex, and although an important issue, food waste is not an everyday cause of concern of an average person.

This thesis takes into account all of the shortcomings of the previous works and aims to create a more sustainable solution by exploring new ways of food inventory management in the households by automating the process so that users don't have to manually enter the data themselves. The proposed solution consists of a device that should be easily mounted into any fridge and acts as an interface between users and their food inventory. The device contains a bar-code scanner for the item input and a back-end that is capable of recognizing the item and can in return show user-friendly and valuable information such as the approximate price of the item, the approximate due date etc. and notifies users when an item is about to expire so that they can take appropriate actions.

7 out of 9 participants in the final conceptual design study said they would use this solution in their homes. The rest of the results from the designed test cases indicate a clear excitement and interest in participants and a willingness to see the prototype in the finished state, all the comments and insights together with the future work and how the feedback will be used into the next iteration are part of the final discussion of this thesis.

Microscope cameras possess high zoom capabilities and are frequently used for quality control in various industries. Visus Technology is a company that manufactures and sells these cameras, along with control software. The problem is that these cameras can only be set to one focus depth at a time. Therefore, we were tasked with implementing a fast focus stacking algorithm that combines multiple input images with different focus depths, resulting in a composite image with consistent focus throughout. This will later be integrated into the control software.

We initially approached the problem by choosing pixels with the highest Laplacian intensity. This resulted in very noisy output images since the blurry images could sometimes produce high-intensity pixels where they were not supposed to be. To address this, we implemented a grid-based focus stacking algorithm. The algorithm divides the input images into tiles, filtering out the blurriest tiles to exclude them from the stacking process. The result is a composite image with significantly reduced blur.

Scientific and technological innovations have become increasingly important as we face the benefits and challenges of both globalization and a knowledge-based economy. Still, enrolment rates in STEM degrees are low in many European countries and consequently there is a lack of adequately educated workforce in industries. We believe that this can be mainly attributed to pedagogical issues, such as the lack of engaging hands-on activities utilized for science and math education in middle and high schools. In this paper, we report our work in the SciChallenge European project, which aims at increasing the interest of pre-university students in STEM disciplines, through its distinguishing feature, the systematic use of social media for providing and evaluation of the student-generated content. A social media-aware contest and platform were thus developed and tested in a pan-European contest that attracted >700 participants. The statistical analysis and results revealed that the platform and contest positively influenced participants STEM learning and motivation, while only the gender factor for the younger study group appeared to affect the outcomes (confidence level – p<.05).

Despite the abundance of research on methodologies for the documentation of design decisions and the evidence linking documentation to the improvement in the systems evolution, their practical adoption seems to be sparse. To understand this issue, we have conducted an overview of state-of-the-art on documentation of design decisions. We pursue an identification of characteristics of the different techniques proposed in the literature, such as the final goal of the documentation, the quantity of information attached to each decision documentation, the rigour of the proposed technique or its level of automation. To unveil these, we propose six classification dimensions, relevant for the industrial application, and use them to structure and analyse the review results. This work contributes with a taxonomy of the area, a structured overview covering 96 publications and a summary of open questions, which can be addressed by future research to facilitate practical adoption.

We have designed and implemented a Computer Aided Personal Interview (CAPI) system that learns from expert interviews and can support less experienced interviewers by for example suggesting questions to ask or skip. We were particularly interested to streamline the due diligence process when estimating the value for software startups. For our design we evaluated some machine learning algorithms and their trade-offs, and in a small case study we evaluates their implementation and performance. We find that while there is room for improvement, the system can learn and recommend questions. The CAPI system can in principle be applied to any domain in which long interview sessions should be shortened without sacrificing the quality of the assessment.

Software architecture is a software engineering discipline that

provides notations and processes for high-level partitioning of

systems' responsibilities early in the software design process. This

thesis is concerned with a specific subclass of systems, systems with a dynamic software architecture. They have practical applications in various domains such as high-availability systems and ubiquitous computing.

In a dynamic software architecture, the set of architectural elements and the configuration of these elements may change at run-time. These modifications are motivated by changed system requirements or by changed execution environments. The implications of change events may be the addition of new functionality or re-configuration to meet new Quality of Service requirements.

This thesis investigates new modeling and implementation techniques for dynamic software architectures. The field of Dynamic Architecture is surveyed and a common ground defined. We introduce new concepts and techniques that simplify understanding, modeling, and implementation of systems with a dynamic architecture, with this common ground as our starting point. In addition, we investigate practical use and reuse of quality implementations, where a dynamic software architecture is a

fundamental design principle.

The main contributions are a taxonomy, a classification, and a set of architectural patterns for dynamic software architecture. The taxonomy and classification support analysis, while the patterns affect design and implementation work directly. The investigation of practical applications of dynamic architectures identifies several issues concerned with use and reuse, and discusses alternatives and solutions where possible.

The results are based on surveys, case studies, and exploratory development of dynamic software architectures in different

application domains using several approaches. The taxonomy,

classification and architecture patterns are evaluated through several experimental prototypes, among others, a high-performance scientific computing platform.

In this paper, we discuss how for self-adaptive systems someactivities that traditionally occur at development-time are moved to runtime. Responsibilities for these activities shift from software engineers tothe system itself, causing the traditional boundary between development time and run-time to blur. As a consequence, we argue how the traditional  software engineering process needs to be reconceptualized to distinguishvboth development-time and run-time activities, and to support designers in taking decisions on how to properly engineer such systems.Furthermore, we identify a number of challenges related to this required reconceptualization, and we propose initial ideas based on process modeling.We use the Software and Systems Process Engineering Meta-Model(SPEM) to specify which activities are meant to be performed o-line andon-line, and also the dependencies between them. The proposed models should capture information about the costs and benets of shifting activitiesto run-time, since such models should support software engineers in their decisions when they are engineering self-adaptive systems.

In recent years, we have observed the increasing interest in the system property resilience. We ascribe this increasing interest to the rapidly growing number of deployed, complex, socio-technical systems, which are facing uncertainty about changes they are expected to experience during their life-cycle and ways to deal with them. This paper contributes to current resilience research by focusing on the different definitions given for this system property, highlighting the risk that, using different terms in different communities, this contributes to create a “tower of Babel” problem, with the consequent difficulty in exchanging ideas and working together towards a common goal. We adopt an extended definition of dependability to define resilience. Based on that, we identify features of resilient systems, capture properties falling under the resilience umbrella, and define a conceptual framework for resilience characterization including how changes affect the system, strategies to design resilience, and discuss metrics for quantifying resilience at design and runtime.

In a service-oriented setting, where services are composed to provide end user functionality, it is a challenge to find the service components with best-fit functionality and quality. A decision based on information mainly provided by service providers is inadequate as it cannot be trusted in general. In this paper, we discuss service compositions in an open market scenario where an automated best-fit service selection and composition is based on Service Level Achievements instead. Continuous monitoring updates the actual Service Level Achievements which can lead to dynamically changing compositions. Measurements of real life services exemplify the approach.

As IT platforms grow larger and more complex, so does the underlying infrastructure. Virtualization is an essential factor for more efficient resource allocation, improving both the management and environmental impact. It allows more robust solutions and facilitates the use of IaC (infrastructure ascode). Many systems developed today consist of containerized microservices. Considered the standard of container orchestration, Kubernetes is the natural next step for many companies. But how do we move on from previous solutions to a Kubernetes cluster? We found that there are not a lot of detailed enough guidelines available, and set out to gain more knowledge by diving into the subject - implementing prototypes that would act as a foundation for a resulting guideline of how it can be done.

Today more and more objects in our daily lives are getting connected to the Internet. This phenomenon is called the Internet of Things and is a way for physical things such as cars, buildings or even bus stations to get access and communicate with other objects using the Internet. The problem is that for every Internet of Things device, an application is often needed in order to communicate with these devices. Developing mobile applications in a separate programming language for each operating system can be an expensive and time consuming task.

In this thesis, we implement and evaluate a cross-platform mobile solution for users to interact with smart things using the advantages of web technologies. To compare previous findings in this area, two literature reviews has been performed to find out which is the state of the art on cross-platform mobile development frameworks and smart-things technologies used for interacting with physical objects. The result is a mobile application developed using PhoneGap and jQuery Mobile that interacts with iBeacons, where students inside a university building can get directions and schedules for different rooms.

The application received good results from a couple of usability studies, and performed well when measuring its performance. The outcome shows that web technologies that exist today are a viable solution to native mobile applications in terms of interacting with smart things such as tagging technologies.

Software Quality Assurance (SQA) is a critical aspect of the software development life cycle, ensuring the delivery of high-quality software products. This systematic literature review provides an overview of product, process and people metrics used in SQA, as well as the relationship between different metrics. Product Metrics focus on evaluating the characteristics and attributes of the software product itself. Pro- cess Metrics centre around the software development and testing processes. People Metrics focuses on evaluating the performance, skills, and collaboration of the indi- viduals involved in the software development team. 

This study not only synthesizes existing knowledge on SQA measurements, but also elucidates the complicated interactions between these many metrics, offering a comprehensive understanding of their interaction. Furthermore, it methodically finds and addresses discrepancies in the identification and description of various in- dicators, helping to standardize SQA methods. 

The paper also covers a range of technologies used for the quantification of the defined metrics with the goal of measurement accuracy and efficiency. The exami- nation of these tools not only aids in the practical comprehension of metric measure- ment but also the selection of appropriate instruments for certain SQA scenarios. 

As software systems grow in complexity and size, the findings of this study are a significant resource for practitioners, researchers, and stakeholders alike, providing a nuanced viewpoint on improving software quality through strong SQA techniques. 

Keywords: Product metrics, Process metrics, people metrics, Software quality as- surance, Development cycle Software Quality Assurance (SQA) Metrics Evaluation Product Metrics Process Metrics People Metrics Software Development Life Cycle (SDLC). 

Software Quality Assurance (SQA) is a critical aspect of the software development life cycle, ensuring the delivery of high-quality software products. This systematic literature review provides an overview of product, process and people metrics used in SQA, as well as the relationship between different metrics. Product Metrics focus on evaluating the characteristics and attributes of the software product itself. Process Metrics centre around the software development and testing processes. PeopleMetrics focuses on evaluating the performance, skills, and collaboration of the individuals involved in the software development team. This study not only synthesizes existing knowledge on SQA measurements but also elucidates the complicated interactions between these many metrics, offering a comprehensive understanding of their interaction. Furthermore, it methodically finds and addresses discrepancies in the identification and description of various indicators, helping to standardize SQA methods. The paper also covers a range of technologies used for the quantification of the defined metrics with the goal of measurement accuracy and efficiency. The examination of these tools not only aids in the practical comprehension of metric measurement but also in the selection of appropriate instruments for certain SQA scenarios. As software systems grow in complexity and size, the findings of this study are significant resources for practitioners, researchers, and stakeholders alike, providing nuanced viewpoints on improving software quality through strong SQA techniques. Keywords: Product metrics, Process metrics, people metrics, Software quality assurance, Development cycle Software Quality Assurance (SQA) Metrics evaluation metrics Process Metrics People Metrics Software Development Life Cycle(SDLC).   

Nowadays, many enterprises commit to the extraction of actionable knowledge from huge datasets as part of their core business activities. Applications belong to very different domains such as fraud detection or one-to-one marketing, and encompass business analytics and support to decision making in both private and public sectors. In these scenarios, a central place is held by the MapReduce framework and in particular its open source implementation, Apache Hadoop. In such environments, new challenges arise in the area of jobs performance prediction, with the needs to provide Service Level Agreement guarantees to the end-user and to avoid waste of computational resources. In this paper we provide performance analysis models to estimate MapReduce job execution times in Hadoop clusters governed by the YARN Capacity Scheduler. We propose models of increasing complexity and accuracy, ranging from queueing networks to stochastic well formed nets, able to estimate job performance under a number of scenarios of interest, including also unreliable resources. The accuracy of our models is evaluated by considering the TPC-DS industry benchmark running experiments on Amazon EC2 and the CINECA Italian supercomputing center. The results have shown that the average accuracy we can achieve is in the range 9–14%.

As part of the DevOps tactics, Infrastructure-as- Code (IaC) provides the ability to create, configure, and manage complex infrastructures by means of executable code. Writing IaC, however, is not an easy task, since it requires blending different infrastructure programming languages and abstractions, each specialized on a particular aspect of infrastructure creation, configuration, and management. Moreover, the more the architectures become large and complex (e.g. Data-Intensive or Microservice-based architectures), the more dire the need of IaC becomes. The goal of this paper is to exploit Model- Driven Engineering (MDE) to create language-agnostic models that are then automatically transformed into IaC. We focus on the domain of Data-Intensive Applications as these typically exploit complex infrastructures which demand sophisticated and fine-grained configuration and re-configuration — we show that, through our approach, called DICER, it is possible to create complex IaC with significant amounts of time savings, both in IaC design as well as deployment and re-deployment times.

Recently multi-core processors have become very popular in computer systems. It allows multiple threads to be executed simultaneously. The advantage of multi-core comes by parallelizing codes to expand the work across hardware. Furthermore, this can be done by using a parallel environment developed by M.I.T. called Intel Cilk Plus, which is design to provide an easy and well-structured parallel programming approach.

     Intel Cilk Plus is an extension of C and C++ programming languages that describes data parallelism. This extension is very helpful and easy to use among other languages in this field. It has different features including keywords, reducers and array notations etc. In general, this article describes Intel Cilk Plus and its features. In addition, Unified Modelling Language, activity diagrams are used in term of graphical modelling of Intel Cilk Plus by describing the process of a system, capturing the dynamic behaviour of it and representing the flow from one activity to another using control flow. Later on Intel Cilk Plus keywords and UML diagrams tools will be evaluated, a comparison of different UML modelling tools will also be provided.

Self-adaptive systems are systems that automatically adapt in response to environmental and internal changes, such as possible failures and variations in resource availability. Such systems are often realized by a MAPE-K feedback loop, where Monitor, Analyze, Plan and Execute components have access to a runtime model of the system and environment which is kept in the Knowledge component. In order to provide guarantees on the correctness of a self-adaptive system at runtime, the MAPE-K feedback loop needs to be extended with assurance techniques. To address this issue, we propose a coordinated actor-based approach to build a reusable and scalable model@runtime for self-adaptive systems in the domain of track-based traffic control systems. We demonstrate the approach by implementing an automated Air Traffic Control system (ATC) using Ptolemy tool.We compare different adaptation policies on the ATC model based on performance metrics and analyze combination of policies in different configurations of the model. We enriched our framework with runtime performance analysis such that for any unexpected change, subsequent behavior of the model is predicted and results are used for adaptation at the change-point. Moreover, the developed framework enables checking safety properties at runtime. © Springer International Publishing AG 2017.

An unavoidable amount of money is spent on maintaining existing software systems today. Software maintenance cost generally higher than development cost of the system therefore lowering maintenance cost is highly appreciated in software industry.

A significant part of maintenance activities is related to repeating the investigation of problems and applying repeated solutions several times. A software system may contain a common bug in several different places and it might take extra effort and time to fix all existences of this bug. This operation commonly increases the cost of Software Maintenance Activities.

Detecting duplicate code fragments can significantly decrease the time and effort therefore the maintenance cost. Clone code detection can be achieved via analyzing the source code of given software system. An abstract syntax tree based clone detector for java systems is designed and implemented through this study.

This master thesis examines a software engineering process to create an abstract syntax tree based clone detector for the projects implemented in Java programming language.

By the increasing needs of software industry, software systems became more complex constructions than ever before. As a result of increasing complexity in software systems, functional decomposition of these systems gains the status of the most important aspect in the software development process. Dividing problems to sub-problems and producing specific solutions for divided parts makes it easier to solve the main problem.

Component Based Software Engineering is a way of developing software systems that consists of logically or functionally decomposed components which integrated to each other by the help of well-defined interfaces. CBSE relies on architectural design of a software system.

Planning phase and implementation of a software project may differ time to time. Because of the complexity of software systems, solving specific problems may affect the architecture of the whole system.

In spite of sophisticated software engineering processes and CASE tools there is still a large gap between the planned and implemented architecture of software systems. Finding deviations from architecture in source code is a non-trivial task requiring tool support.

Since, matching operation of designed software architecture and implemented software architecture needs to check design documents against implementation code. This manual checking operation is nearly impossible for major software systems. Software Architecture Checker provides a great approach to check the architecture of any software system.

This bachelor thesis examines the approach behind the Software Architecture Checker.

In recent years, we have seen many performance fiascos in the deployment of new systems, such as the US health insurance web. This paper describes the functionality and architecture, as well as success stories, of a tool that helps address these types of issues. The tool allows assessing software designs regarding quality, in particular performance and reliability. Starting from a UML design with quality annotations, the tool applies model-transformation techniques to yield analyzable models. Such models are then leveraged by the tool to compute quality metrics. Finally, quality results, over the design, are presented to the engineer, in terms of the problem domain. Hence, the tool is an asset for the software engineer to evaluate system quality through software designs. While leveraging the Eclipse platform, the tool uses UML and the MARTE, DAM and DICE profiles for the system design and the quality modeling.

Managing the architectural description (AD) of a complex software system and maintaining consistency among the different models is a demanding task. To understand the underlying problems, we analyse several non-trivial software architectures. The empirical study shows that a substantial amount of information of ADs is repeated, mainly by integrating information of different models in new models. Closer examination reveals that the absence of rigorously specified dependencies among models and the lack of support for automated composition of models are primary causes of management and consistency problems in software architecture. To tackle these problems, we introduce an approach in which compositions of models, together with relations among models, are explicitly supported in the ADL. We introduce these concepts formally and discuss a proof-of-concept instantiation of composition in xADL and its supporting tools. The approach is evaluated by comparing the original and revised ADs in an empirical study. The study indicates that our approach reduces the number of manually specified elements by 29%, and reduces the number of manual changes to elements for several realistic change scenarios by 52%.

Our position is that architectural descriptions lackcomposition of views, preventing a proper separation of concerns.This position took shape from experiences with buildingseveral complex distributed software systems. Our claim is thatview composition should be a first-class entity in architecturaldescriptions. As a first step, we propose an extension of the conceptualmodel of IEEE Recommended Practice for ArchitecturalDescription of Software-Intensive Systems with view composition.

Cyber-Physical Systems (CPS) are large interconnected softwareintensivesystems that influence, by sensing and actuating, thephysical world. Examples are traffic management and power grids.One of the trends we observe is the need to endow such systemswith the “smart” capabilities, typically in the form of selfawarenessand self-adaptation, along with the traditional qualitiesof safety and dependability. These requirements combined withspecifics of the domain of smart CPS – such as large scale, the roleof end-users, uncertainty, and open-endedness – render traditionalsoftware engineering (SE) techniques not directly applicable; makingsystematic SE of smart CPS a challenging task. This paperreports on the results of the First International Workshop on SoftwareEngineering of Smart Cyber-Physical Systems (SEsCPS2015), where participants discussed characteristics, challenges andopportunities of SE for smart CPS, with the aim to outline anagenda for future research in this important area.

Cyber-physical system (CPS) have been recognized as a top-priority in research and development. The innovations sought for CPS demand them to deal effectively with dynamicity of their environment, to be scalable, adaptive, tolerant to threats, etc. -- i.e. they have to be smart. Although approaches insoftware engineering (SE) exist that individually meet these demands, their synergy to address the challenges of smart CPS (sCPS) in a holistic manner remains an open challenge. The workshop focuses on software engineering challenges for sCPS. The goals are to increase the understanding of problems of SE for sCPS, study foundational principles for engineering sCPS, and identify promising SE solutions for sCPS. Based on these goals, the workshop aims to formulate a research agenda for SE of sCPS.

Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.

Model View Controller (MVC) software architecture is widely spread and commonly used in application’s development. Therefore generation of data layer for the database model is able to reduce cost and time. After research on current Object Relational Mapping (ORM) tools, it was discovered that there are generating tools like Data Access Object (DAO) and Hibernate, however their usage causes problems like inefficiency and slow performance due to many connections with database and set up time. Most of these tools are trying to solve specific problems rather than generating a data layer which is an important component and the bottom layer of database centred applications.

The proposed solution to the problem is an engineering approach where we have designed a tool named Generated Intelligent Data Layer (GIDL). GIDL tool generates small models which create the main data layer of the system according to the Database Model. The goal of this tool is to enable and allow software developers to work only with object without deep knowledge in SQL.

The problem of transaction and commit is solved by the tool. Also filter objects are constructed for filtering the database. GIDL tool reduced the number of connections and also have a cache where to store object lists and modify them. The tool is compared under the same environment with Hibernate and showed a better performance in terms of time evaluations for the same functions. GIDL tool is beneficial for software developers, because it generates the entire data layer.

There are many aspects of code quality, some of which are difficult to capture or to measure. Despite the importance of software quality, there is a lack of commonly accepted measures or indicators for code quality that can be linked to quality attributes. We investigate software developers’ perceptions of source code quality and the practices they recommend to achieve these qualities. We analyze data from semi-structured interviews with 34 professional software developers, programming teachers and students from Europe and the U.S. For the interviews, participants were asked to bring code examples to exemplify what they consider good and bad code, respectively. Readability and structure were used most commonly as defining properties for quality code. Together with documentation, they were also suggested as the most common target properties for quality improvement. When discussing actual code, developers focused on structure, comprehensibility and readability as quality properties. When analyzing relationships between properties, the most commonly talked about target property was comprehensibility. Documentation, structure and readability were named most frequently as source properties to achieve good comprehensibility. Some of the most important source code properties contributing to code quality as perceived by developers lack clear definitions and are difficult to capture. More research is therefore necessary to measure the structure, comprehensibility and readability of code in ways that matter for developers and to relate these measures of code structure, comprehensibility and readability to common software quality attributes.

A traditional approach to realize self-adaptation in software engineering (SE) is by means of feedback loops. The goals of the system can be specified as formal properties that are verified against models of the system. On the other hand, control theory (CT) provides a well-established foundation for designing feedback loop systems and providing guarantees for essential properties, such as stability, settling time, and steady state error. Currently, it is an open question whether and how traditional SE approaches to self-adaptation consider properties from CT. Answering this question is challenging given the principle differences in representing properties in both fields. In this paper, we take a first step to answer this question. We follow a bottom up approach where we specify a control design (in Simulink) for a case inspired by Scuderia Ferrari (F1) and provide evidence for stability and safety. The design is then transferred into code (in C) that is further optimized. Next, we define properties that enable verifying whether the control properties still hold at code level. Then, we consolidate the solution by mapping the properties in both worlds using specification patterns as common language and we verify the correctness of this mapping. The mapping offers a reusable artifact to solve similar problems. Finally, we outline opportunities for future work, particularly to refine and extend the mapping and investigate how it can improve the engineering of self-adaptive systems for both SE and CT engineers. © 2021 IEEE.

Building on concepts drawn from control theory, self-adaptive software handles environmental and internal uncertainties by dynamically adjusting its architecture and parameters in response to events such as workload changes and component failures. Self-adaptive software is increasingly expected to meet strict functional and non-functional requirements in applications from areas as diverse as manufacturing, healthcare and finance. To address this need, we introduce a methodology for the systematic ENgineering of TRUstworthy Self-adaptive sofTware (ENTRUST). ENTRUST uses a combination of (1) design-time and runtime modelling and verification, and (2) industry-adopted assurance processes to develop trustworthy self-adaptive software and assurance cases arguing the suitability of the software for its intended application. To evaluate the effectiveness of our methodology, we present a tool-supported instance of ENTRUST and its use to develop proof-of-concept self-adaptive software for embedded and service-based systems from the oceanic monitoring and e-finance domains, respectively. The experimental results show that ENTRUST can be used to engineer self-adaptive software systems in different application domains and to generate dynamic assurance cases for these systems.

Service discovery mechanisms have continuously evolved during the last years to support the effective and efficient service composition in large-scale microservice applications. Still, the dynamic nature of services (and of their contexts) are being rarely taken into account for maximizing the desired quality of service. This paper proposes using machine learning techniques, as part of the service discovery process, to select microservice instances in a given context, maximize QoS, and take into account the continuous changes in the execution environment. Both deep neural networks and reinforcement learning techniques are used. Experimental results show how the proposed approach outperforms traditional service discovery mechanisms.

Pervasive systems are often made out of distributed software components that run on different computational units (appliances, sensing and actuating devices, computers). Such components are often developed, maintained, and even operated by different parties. Applications are increasingly built by dynamically discovering and composing such components in a situation-aware manner. By this we mean that applications follow some strategies to self-organize themselves to adapt their behavior depending on the changing situation in which they operate, for example the physical environment. They may also evolve autonomously in response to changing requirements. Software architectures are considered a well-suited abstraction to achieve situational adaptation. In this paper, we review some existing architectural approaches to self-adaptation and propose a high-level meta-model for architectures that supports dynamic adaptation. The meta-model is then instantiated in a specific ambient computing case study, which is used to illustrate its applicability.

Computing facilities are an essential part of the fabric of our society, and an ever-increasing number of computing devices is deployed within the environment in which we live. The vision of pervasive computing is becoming real. To exploit the opportunities offered by pervasiveness, we need to revisit the classic software development methods to meet new requirements: (1) pervasive applications should be able to dynamically configure themselves, also benefiting from third-party functionalities discovered at run time and (2) pervasive applications should be aware of, and resilient to, environmental changes. In this chapter we focus on the software architecture, with the goal of facilitating both the development and the run-time adaptation of pervasive applications. More specifically we investigate the adoption of the REST architectural style to deal with pervasive environment issues. Indeed, we believe that, although REST has been introduced by observing and analyzing the structure of the Internet, its field of applicability is not restricted to it. The chapter also illustrates a proof-of-concept example, and then discusses the advantages of choosing REST over other styles in pervasive environments.

In this paper we present a compositional reasoning to verify middleware-based software architecturedescriptions. We consider a nowadays typical software system development, namely the development of a software application A on a middleware M. Our goal is to efficiently integrate verification techniques, like model checking, in the software life cycle in order to improve the overall software quality. The approach exploits the structure imposed on the system by the software architecture in order to develop an assume-guarantee methodology to reduce properties verification from global to local. We apply the methodology on a non-trivial case study namely the development of a Gnutella system on top of the SIENA event-notification middleware.

B3G is an emerging network technology which conceives the convergence of telecommunication and IP-based networks for providing enhanced services able to transfer both voice and non-voice data through wired and wireless networks. Moreover, B3G networks can be no longer considered as “passive” entities which only transport data between end-points, but they must be considered as “active” parties that have their own behavior and provide services. This creates a completely new application domain where applying current software engineering design tools, such as software architectures, fails. In fact, dealing with B3G networks requires to explicit low-level details usually abstracted by the architectural descriptions.

To this extent, we present an ongoing work on investigating B3G-oriented application modeling. In particular, we propose an enhanced UML profile to define and analyze software architectures that explicitly exploit the B3G domain properties.

Recently, growing attention focused on run-time management of Quality of Service (QoS) of complex software systems. In this context, system reconfiguration is considered a useful technique to manage QoS. Several reconfiguration approaches to performance management exist that help systems to maintain performance requirements at run time. However, many of them use prefixed strategies that are in general coded in the application or in the reconfiguration framework.

In this work we propose a framework to manage performance of software systems at run time based on monitoring and model-based performance evaluation. The approach makes use of software architectures as abstractions of the managed system to avoid unnecessary details that can heavily affect the model evaluation in terms of complexity and resolution time.

Recently, growing attention is focused on run-time management of Quality of Service (QoS) of complex software systems. In this context, self-adaptation of applications, based on runtime monitoring and dynamic reconfiguration, is considered a useful technique to manage/negotiate QoS. Several defined approaches to dynamic performance management propose the use of on-line model-based evaluation of the managed application in order to choose the new application configuration for improving the QoS. In a previous work we defined a methodology to manage performance of soft-ware system at run-time based on model-based performance evaluation. To avoid unnecessary details the performance model represents the managed application at the Software Architecture level. In this work we apply and implement this approach to dynamic manage the performance of the SIENA publish/subscribe event notification service.

The Service Oriented Architecture is boosting a fast move from developing applications as stand-alone systems, to developing applications as composition of autonomous and heterogeneous services. Service compositions are required to adapt dynamically to changing workloads, scenarios and objectives, and to achieve a certain Quality of Service (QoS). Guaranteeing such requirements is not a trivial task, since run-time variability makes the process of devising service compositions challenging for software designers. In this paper, we exploit the QoS analysis at run time to support software design, highlighting service compositions where QoS predictions are not reliable enough. To this end, we propose a QoS-based feedback framework that makes use of design-time and run-time knowledge to manage QoS data over time, and support software architect while devising a service composition that best fits QoS requirements. We illustrate the feasibility and effectiveness of the approach on a case study from the e-health domain.

Software Product Line (SPL) engineering allows designers to reason about an entire family of software applications, instead of a single product, with a strategic importance for the rapid development of new applications. While much effort has been spent so far in understanding and modeling SPLs and their architectures, very little attention has been given on how to systematically enforce SPL architectural decisions into the implementation step.

In this paper we propose a methodological approach and an implementation framework, based on a plugin component-based development, which allows us to move from an architectural specification of the SPL to its implementation in a systematic way. We show the suitability of this framework through its application to the TOOL one case study SPL.

The computing and networking capacities of today’s wireless portable devices allow for pervasive services, which are seamlessly networked. Indeed, wireless handheld devices now embed the necessary resources to act as both service clients and providers. However, the seamless networking of services remains challenged by the inherent mobility and resource constraints of devices, which make services a priori highly volatile. This paper discusses the design, implementation and experimentation of the ubiSOAP service-oriented middleware, which leverages wireless networking capacities to effectively enable the seamless networking of services. ubiSOAP specifically defines a layered communication middleware that underlies standard SOAP-based middleware, hence supporting legacy services while exploiting nowadays pervasive connectivity.