Modern software systems are expected to operate under uncertain conditions, without interruption. Possible causes of uncertainties include changes in the operational environment, dynamics in the availability of resources, and variations of user goals. The aim of self-adaptation is to let the system collect additional data about the uncertainties during operation. The system uses the additional data to resolve uncertainties, to reason about itself, and based on its goals to reconfigure or adjust itself to satisfy the changing conditions, or if necessary to degrade gracefully. In this chapter, we provide a particular perspective on the evolution of the field of self-adaptation in six waves. These waves put complementary aspects of engineering self-adaptive systems in focus that synergistically have contributed to the current knowledge in the field. From the presented perspective on the field, we outline a number of challenges for future research in self-adaptation, both in a short and long term.

Software Engineering of Self-adaptive Systems

Software testing involves probing into the behavior of software systems to uncover faults. Most testing activities are complex and costly, so a practical strategy that has been adopted to circumvent these issues is to automate software testing. There has been a growing interest in applying machine learning (ML) to automate various software engineering activities, including testing-related ones. In this paper, we set out to review the state-of-the art of how ML has been explored to automate and streamline software testing and provide an overview of the research at the intersection of these two fields by conducting a systematic mapping study. We selected 48 primary studies. These selected studies were then categorized according to study type, testing activity, and ML algorithm employed to automate the testing activity. The results highlight the most widely used ML algorithms and identify several avenues for future research. We found that ML algorithms have been used mainly for test-case generation, refinement, and evaluation. Also, ML has been used to evaluate test oracle construction and to predict the cost of testing-related activities. The results of this paper outline the ML algorithms that are most commonly used to automate software-testing activities, helping researchers to understand the current state of research concerning ML applied to software testing. We also found that there is a need for better empirical studies examining how ML algorithms have been used to automate software-testing activities.

Machine Learning Applied to Software Testing: A Systematic Mapping Study

Model-driven engineering (MDE) and search-based software engineering (SBSE) are both relevant approaches to software engineering. MDE aims to raise the level of abstraction in order to cope with the complexity of software systems, while SBSE involves the application of metaheuristic search techniques to complex software engineering problems, reformulating engineering tasks as optimization problems. The purpose of this paper is to survey the relatively recent research activity lying at the interface between these two fields, an area that has come to be known as search-based model-driven engineering. We begin with an introduction to MDE, the concepts of models, of metamodels and of model transformations. We also give a brief introduction to SBSE and metaheuristics. Then, we survey the current research work centered around the combination of search-based techniques and MDE. The literature survey is accompanied by the presentation of references for further details.

A survey on search-based model-driven engineering

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

https://www.mdpi.com/1424-8220/19/9/2025/pdf

Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

/pdf/a-literature-review-of-using-machine-learning-in-software-2fwgxw8wik.pdf

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

/pdf/the-role-of-artificial-intelligence-in-software-engineering-48zw6ek5de.pdf

The role of Artificial Intelligence in Software Engineering

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The role of Artificial Intelligence in Software Engineering