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  • 1.
    Abbas, Nadeem
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Designing Self-Adaptive Software Systems with Reuse2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    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.

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    Doctoral Thesis (Comprehensive Summary)
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  • 2.
    Abbas, Nadeem
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Towards autonomic software product lines2011In: SPLC '11 Proceedings of the 15th International Software Product Line Conference, Volume 2, ACM Press, 2011, p. 44:1-44:8Conference paper (Refereed)
    Abstract [en]

    We envision an Autonomic Software Product Line (ASPL). The ASPL is a dynamic software product line that supports self adaptable products. We plan to use reflective architecture to model and develop ASPL. To evaluate the approach, we have implemented three autonomic product lines which show promising results. The ASPL approach is at initial stages, and require additional work. We plan to exploit online learning to realize more dynamic software product lines to cope with the problem of product line evolution. We propose on-line knowledge sharing among products in a product line to achieve continuous improvement of quality in product line products.

  • 3.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Andersson, Jesper
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Architectural reasoning for dynamic software product lines2013In: Proceedings of the 17th International Software Product Line Conference co-located workshops, ACM Press, 2013, p. 117-124Conference paper (Refereed)
    Abstract [en]

    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.

  • 4.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Andersson, Jesper
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Architectural Reasoning Support for Product-Lines of Self-adaptive Software Systems: A Case Study2015In: Software Architecture: 9th European Conference, ECSA 2015, Dubrovnik/Cavtat, Croatia, September 7-11, 201 / [ed] Danny Weyns, Raffaela Mirandola, Ivica Crnkovic, Springer, 2015, p. 20-36Conference paper (Refereed)
    Abstract [en]

    Software architecture serves as a foundation for the design and development of software systems. Designing an architecture requires extensive analysis and reasoning. The study presented herein focuses on the architectural analysis and reasoning in support of engineering self-adaptive software systems with systematic reuse. Designing self-adaptive software systems with systematic reuse introduces variability along three dimensions; adding more complexity to the architectural analysis and reasoning process. To this end, the study presents an extended Architectural Reasoning Framework with dedicated reasoning support for self-adaptive systems and reuse. To evaluate the proposed framework, we conducted an initial feasibility case study, which concludes that the proposed framework assists the domain architects to increase reusability, reduce fault density, and eliminate differences in skills and experiences among architects, which were our research goals and are decisive factors for a system's overall quality.

  • 5.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Andersson, Jesper
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    ASPLe: a methodology to develop self-adaptive software systems with reuse2017Report (Other academic)
    Abstract [en]

    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.

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    ASPLe2017
  • 6.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Andersson, Jesper
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Harnessing Variability in Product-lines of Self-adaptive Software Systems2015In: Proceedings of the 19th International Conference on Software Product Line: SPLC '15, ACM Press, 2015, p. 191-200Conference paper (Refereed)
    Abstract [en]

    This work studies systematic reuse in the context of self-adaptive software systems. In our work, we realized that managing variability for such platforms is different compared to traditional platforms, primarily due to the run-time variability and system uncertainties. Motivated by the fact that recent trends show that self-adaptation will be used more often in future system generation and that software reuse state-of-practice or research do not provide sufficient support, we have investigated the problems and possibly resolutions in this context. We have analyzed variability for these systems, using a systematic reuse prism, and identified a research gap in variability management. The analysis divides variability handling into four activities: (1) identify variability, (2) constrain variability, (3) implement variability, and (4) manage variability. Based on the findings we envision a reuse framework for the specific domain and present an example framework that addresses some of the identified challenges. We argue that it provides basic support for engineering self-adaptive software systems with systematic reuse. We discuss some important avenues of research for achieving the vision.

  • 7.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Andersson, Jesper
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Iftikhar, Muhammad Usman
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Weyns, Danny
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Rigorous architectural reasoning for self-adaptive software systems2016In: Proceedings: First Workshop on Qualitative Reasoning abut Software Architectures, QRASA 2016 / [ed] Lisa O'Conner, IEEE, 2016, p. 11-18Conference paper (Refereed)
    Abstract [en]

    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.

  • 8.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Andersson, Jesper
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Löwe, Welf
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Autonomic Software Product Lines (ASPL)2010In: ECSA '10 Proceedings of the Fourth European Conference on Software Architecture: Companion Volume / [ed] Carlos E. Cuesta, ACM Press, 2010, p. 324-331Conference paper (Refereed)
    Abstract [en]

    We describe ongoing work on a variability mechanism for Autonomic Software Product Lines (ASPL). The autonomic software product lines have self-management characteristics that make product line instances more resilient to context changes and some aspects of product line evolution. Instances sense the context, selects and bind the best component variants to variation-points at run-time. The variability mechanism we describe is composed of a profile guided dispatch based on off-line and on-line training processes. Together they form a simple, yet powerful variability mechanism that continuously learns, which variants to bind given the current context and system goals.

  • 9.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Andersson, Jesper
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Löwe, Welf
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Towards Autonomic Software Product Lines (ASPL) - A Technical Report2011Report (Other academic)
    Abstract [en]

    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. 

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    fulltext
  • 10.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Andersson, Jesper
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Weyns, Danny
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Katholieke Univ Leuven, Belgium.
    ASPLe: a methodology to develop self-adaptive software systems with systematic reuse2020In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 167, p. 1-19, article id 110626Article in journal (Refereed)
    Abstract [en]

    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.

  • 11.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Andersson, Jesper
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Weyns, Danny
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Knowledge evolution in autonomic software product lines2011In: SPLC '11 Proceedings of the 15th International Software Product Line Conference, Volume 2, New York, NY, USA: ACM Press, 2011, p. 36:1-36:8Conference paper (Refereed)
    Abstract [en]

    We describe ongoing work in knowledge evolution management for autonomic software product lines. We explore how an autonomic product line may benefit from new knowledge originating from different source activities and artifacts at run time. The motivation for sharing run-time knowledge is that products may self-optimize at run time and thus improve quality faster compared to traditional software product line evolution. We propose two mechanisms that support knowledge evolution in product lines: online learning and knowledge sharing. We describe two basic scenarios for runtime knowledge evolution that involves these mechanisms. We evaluate online learning and knowledge sharing in a small product line setting that shows promising results.

  • 12.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Andersson, Jesper
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Weyns, Danny
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Modeling Variability in Product Lines Using Domain Quality Attribute Scenarios2012In: Proceedings of the WICSA/ECSA 2012 Companion Volume, ACM Press, 2012, p. 135-142Conference paper (Refereed)
    Abstract [en]

    The concept of variability is fundamental in software product lines and a successful implementation of a product line largely depends on how well domain requirements and their variability are specified, managed, and realized. While developing an educational software product line, we identified a lack of support to specify variability in quality concerns. To address this problem we propose an approach to model variability in quality concerns, which is an extension of quality attribute scenarios. In particular, we propose domain quality attribute scenarios, which extend standard quality attribute scenarios with additional information to support specification of variability and deriving product specific scenarios. We demonstrate the approach with scenarios for robustness and upgradability requirements in the educational software product line.

  • 13.
    Abbas, Nadeem
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Awais, Mian Muhammad
    Lahore University of Management Sciences, Pakistan.
    Kurti, Arianit
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Smart Forest Observatories Network: A MAPE-K Architecture Based Approach for Detecting and Monitoring Forest Damage2023In: Proceedings of the Conference Digital solutions for detecting and monitoring forest damage: Växjö, Sweden, March 28-29, 2023, 2023Conference paper (Other academic)
    Abstract [en]

    Forests are essential for life, providing various ecological, social, and economic benefits worldwide. However, one of the main challenges faced by the world is the forest damage caused by biotic and abiotic factors. In any case, the forest damages threaten the environment, biodiversity, and ecosystem. Climate change and anthropogenic activities, such as illegal logging and industrial waste, are among the principal elements contributing to forest damage. To achieve the United Nations' Sustainable Development Goals (SDGs) related to forests and climate change, detecting and analyzing forest damages, and taking appropriate measures to prevent or reduce the damages are essential. To that end, we envision establishing a Smart Forest Observatories (SFOs) network, as shown below, which can be either a local area or a wide area network involving remote forests. The basic idea is to use Monitor, Analyze, Plan, Execute, and Knowledge (MAPE-K) architecture from autonomic computing and self-adaptive software systems domain to design and develop the SFOs network. The SFOs are planned to collect, analyze, and share the collected data and analysis results using state-of-the-art methods. The principal objective of the SFOs network is to provide accurate and real-time data to policymakers and forest managers, enabling them to develop effective policies and management strategies for global forest conservation that help to achieve SDGs related to forests and climate change.

  • 14.
    Talpur, Hafsa
    et al.
    Mehran University of Engineering and Technology, Pakistan.
    Muneer, Badar
    Antennas and Microwave Laboratory, India.
    Memon, Mohsin Ali
    Mehran University of Engineering and Technology, Pakistan.
    Abbas, Nadeem
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Waqas, Abi
    Mehran University of Engineering and Technology, Pakistan.
    AI-Powered Antennas and Microwave Components2023In: AI and Its Convergence With Communication Technologies / [ed] Badar Muneer, Faisal Karim Shaikh, Naeem Mahoto, Shahnawaz Talpur, Jordi Garcia, IGI Global, 2023, p. 97-136Chapter in book (Other academic)
    Abstract [en]

    In wireless communication systems, high-performance antenna, microwave, and radio frequency design systems are essential to meet end-user requirements. As demand for these components increases, it's crucial to design optimized structures in a short amount of time with guaranteed best results. This has led to the need for a higher level of intelligence in the design process. Artificial intelligence (AI) techniques such as evolutionary algorithms (EAs), machine learning (ML), deep learning (DL), and knowledge representation have been widely used to find parameter values of antenna and microwave components, leading to optimized designs in minimum processing time and overcoming long processing times and poor results. This chapter focuses on the major AI methods in the area of antenna, microwave, and other radio frequency (RF) components, including phase shifters, intelligent reflective surfaces (RIS), waveguides, filters, stubs, etc. The chapter discusses different EAs and ML algorithms and their use in optimizing antenna and microwave designs.

  • 15.
    Weyns, Danny
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Katholieke Univ Leuven, Belgium.
    Gerostathopoulos, Ilias
    Vrije Univ Amsterdam, Netherlands.
    Abbas, Nadeem
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Andersson, Jesper
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Biffl, Stefan
    TU Wien, Austria.
    Brada, Premek
    Univ West Bohemia, Czech Republic.
    Bures, Tomas
    Charles Univ Prague, Czech Republi.
    Di Salle, Amleto
    European Univ Rome, Italy.
    Galster, Matthias
    Univ Canterbury, New Zealand.
    Lago, Patricia
    Vrije Univ Amsterdam, Netherlands.
    Lewis, Grace
    Carnegie Mellon Software Engn Inst, USA.
    Litoiu, Marin
    York Univ, Canada.
    Musil, Angelika
    Katholieke Univ Leuven, Belgium;TU Wien, Austria.
    Musil, Juergen
    TU Wien, Austria.
    Patros, Panos
    Raygun Applicat Performance, New Zealand.
    Pelliccione, Patrizio
    Gran Sasso Sci Inst, Italy.
    Self-Adaptation in Industry: A Survey2023In: ACM Transactions on Autonomous and Adaptive Systems, ISSN 1556-4665, E-ISSN 1556-4703, Vol. 18, no 2, article id 5Article in journal (Refereed)
    Abstract [en]

    Computing systems form the backbone of many areas in our society, from manufacturing to traffic control, healthcare, and financial systems. When software plays a vital role in the design, construction, and operation, these systems are referred to as software-intensive systems. Self-adaptation equips a software-intensive system with a feedback loop that either automates tasks that otherwise need to be performed by human operators or deals with uncertain conditions. Such feedback loops have found their way to a variety of practical applications; typical examples are an elastic cloud to adapt computing resources and automated server management to respond quickly to business needs. To gain insight into the motivations for applying self-adaptation in practice, the problems solved using self-adaptation and how these problems are solved, and the difficulties and risks that industry faces in adopting self-adaptation, we performed a large-scale survey. We received 184 valid responses from practitioners spread over 21 countries. Based on the analysis of the survey data, we provide an empirically grounded overview the of state of the practice in the application of self-adaptation. From that, we derive insights for researchers to check their current research with industrial needs, and for practitioners to compare their current practice in applying self-adaptation. These insights also provide opportunities for applying self-adaptation in practice and pave the way for future industry-research collaborations.

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    fulltext
  • 16.
    Weyns, Danny
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Katholieke Universiteit Leuven, Belgium.
    Gerostathopoulos, Ilias
    Vrije Universiteit Amsterdam, Netherlands.
    Abbas, Nadeem
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Andersson, Jesper
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Biffl, Stefan
    CDL-SQI Tu Wien, Austria.
    Brada, Premek
    University of West Bohemia, Czech Republic.
    Bures, Tomas
    Charles University, Czech Republic.
    Salle, Amleto Di
    University of L'Aquila, Italy.
    Lago, Patricia
    Vrije Universiteit Amsterdam, Netherlands.
    Musil, Angelika
    Katholieke Universiteit Leuven, Belgium.
    Musil, Juergen
    CDL-SQI Tu Wien, Austria.
    Pelliccione, Patrizio
    Gran Sasso Science Institute, Italy.
    Preliminary Results of a Survey on the Use of Self-Adaptation in Industry2022In: Proceedings - 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022, IEEE, 2022, p. 70-76Conference paper (Refereed)
    Abstract [en]

    Self-Adaptation equips a software system with a feedback loop that automates tasks that otherwise need to be performed by operators. Such feedback loops have found their way to a variety of practical applications, one typical example is an elastic cloud. Yet, the state of the practice in self-Adaptation is currently not clear. To get insights into the use of self-Adaptation in practice, we are running a largescale survey with industry. This paper reports preliminary results based on survey data that we obtained from 113 practitioners spread over 16 countries, 62 of them work with concrete self-Adaptive systems. We highlight the main insights obtained so far: motivations for self-Adaptation, concrete use cases, and difficulties encountered when applying self-Adaptation in practice. We conclude the paper with outlining our plans for the remainder of the study. © 2022 ACM.

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