Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm

From formal and practical analysis, we identify new challenges that self-adaptive systems pose to the process of quality assurance. When tackling these, the effort spent on various tasks in the process of software engineering is naturally re-distributed. We claim that all steps related to testing need to become self-adaptive to match the capabilities of the self-adaptive system-under-test. Otherwise, the adaptive system's behavior might elude traditional variants of quality assurance. We thus propose the paradigm of scenario coevolution, which describes a pool of test cases and other constraints on system behavior that evolves in parallel to the (in part autonomous) development of behavior in the system-under-test. Scenario coevolution offers a simple structure for the organization of adaptive testing that allows for both human-controlled and autonomous intervention, supporting software engineering for adaptive systems on a procedural as well as technical level.Comment: 17 pages, published at ISOLA 201

Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad

Cultural evolution developing its own rules: The rise of conservatism and persuasion

In the human sciences, cultural evolution is often viewed as an autonomous process free of genetic influence. A question that follows is, If culture is not influenced by genes, can it take any path? Employing a simple mathematical model of cultural transmission in which individuals may copy each other's traits, we show that cultural evolution favors individuals who are weakly influenced by others and able to influence others. The model suggests that the cultural evolution of rules of cultural transmission tends to create populations that evolve rapidly toward conservatism, and that bias in cultural transmission may result purely from cultural dynamics. Freedom from genetic influence is not freedom to take any direction

Autonomous virulence adaptation improves coevolutionary optimization

A novel approach for the autonomous virulence adaptation (AVA) of competing populations in a coevolutionary optimization framework is presented. Previous work has demonstrated that setting an appropriate virulence, v, of populations accelerates coevolutionary optimization by avoiding detrimental periods of disengagement. However, since the likelihood of disengagement varies both between systems and over time, choosing the ideal value of v is problematic. The AVA technique presented here uses a machine learning approach to continuously tune v as system engagement varies. In a simple, abstract domain, AVA is shown to successfully adapt to the most productive values of v. Further experiments, in more complex domains of sorting networks and maze navigation, demonstrate AVA's efficiency over reduced virulence and the layered Pareto coevolutionary archive.A novel approach for the autonomous virulence adaptation (AVA) of competing populations in a coevolutionary optimization framework is presented. Previous work has demonstrated that setting an appropriate virulence, v, of populations accelerates coevolutionary optimization by avoiding detrimental periods of disengagement. However, since the likelihood of disengagement varies both between systems and over time, choosing the ideal value of v is problematic. The AVA technique presented here uses a machine learning approach to continuously tune v as system engagement varies. In a simple, abstract domain, AVA is shown to successfully adapt to the most productive values of v. Further experiments, in more complex domains of sorting networks and maze navigation, demonstrate AVA's efficiency over reduced virulence and the layered Pareto coevolutionary archive

The coevolution of industries and national institutions: Theory and evidence

A survey across space and time reveals that leading firms operating in global industries often cluster in one or a few countries. The paper argues that nations differ in how successful they are in a particular industry because coevolutionary processes linking a particular industry and national institutions powerfully shape the path of an industry.s development. Across a wide range of contexts, scientific progress and industrial leadership reinforce each other in spirals of cumulative national advantage. A historical case study of synthetic dyes from 1857 to 1914 provides a dramatic example of how these positive feedback processes gave German organic chemistry and German dye firms a dominant position in the world. Over time, the relative strength of a nation in a particular industry and the capability of the country in a relevant scientific or engineering discipline display a strong positive correlation. Additional shorter case studies of agriculture, packaged software, and biotechnology support this induced hypothesis. We argue that the exchange of personnel between industry and academic institutions, the formation of commercial ties between them, lobbying on each other.s behalf and direct support from state agencies constitute causal mechanisms that can explain why successful firms often cluster in particular countries. -- Die führenden Unternehmen eines Industriezweiges konzentrieren sich, obwohl sie auf einem internationalen oder globalen Markt agieren, oft nur in einer eng begrenzten Anzahl von Ländern . oder in nur einem Land. Auf der Grundlage verschiedener Fallstudien werden in diesem Artikel spezifische Verknüpfungen von Industrie und national geprägter Wissenschaftslandschaft aufgezeigt, die in einem Prozess enger gegenseitiger Einflussnahme zu einer jeweils herausragenden . dominanten . wirtschaftlichen Position führten. Die Untersuchung der internationalen Dominanz Deutschlands auf dem Gebiet der Herstellung synthetischer Farbstoffe vor dem Ersten Weltkrieg zeigt eine starke positive Wechselwirkung zwischen der Forschung auf dem Gebiet der organischen Chemie und der Marktstellung der farbstoffproduzierenden Unternehmen. Der Aufstieg bedeutender Unternehmen wie Bayer, BASF und Hoechst steht dabei über personellen Austausch, kommerzielle Beziehungen und gemeinsames Lobbying in so enger Verbindung zu den relevanten akademischen Institutionen und ihrer Entwicklung, daß von einem koevolutionären Prozess gesprochen werden kann. Eine derartige positive Korrelation und ein daraus entstehender spezifischer Vorteil wird durch die Betrachtung des Marktes für Computer-Software oder des derzeit vieldiskutierten Bereichs der Biotechnologie untermauert.Industry evolution,national institution,science-industry interface

Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

This paper considers sensemaking as it relates to everyday software engineering (SE) work practices and draws on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with artificial intelligence (AI) and machine learning (ML) capabilities. Our findings highlight the breadth of sensemaking practices in AI/ML projects, noting developers' efforts to make sense of AI/ML environments (e.g., algorithms/methods and libraries), of AI/ML model ecosystems (e.g., pre-trained models and "upstream"models), and of business-AI relations (e.g., how the AI/ML service relates to the domain context and business problem at hand). This paper builds on recent scholarship drawing attention to the integral role of sensemaking in everyday SE practices by empirically investigating how and in what ways AI/ML projects present software teams with emergent sensemaking requirements and opportunities