ADAPT: An agent-based development toolkit and operation platform for self-adaptive systems

Traditional software engineering approaches and metaphors fall short when applied to areas of growing relevance such as electronic commerce, enterprise resource planning, and mobile computing: such areas, in fact, generally call for open architectures that may evolve dynamically over time so as to accommodate new components and meet new requirements. This is probably one of the main reasons that the agent metaphor and the agent-oriented paradigm are gaining momentum in these areas. This thesis deals with the engineering of complex software systems in terms of the agent paradigm. This paradigm is based on the notions of agent and systems of interacting agents as fundamental abstractions for designing, developing and managing at runtime typically distributed software systems. However, today the engineer often works with technologies that do not support the abstractions used in the design of the systems. For this reason the research on methodologies becomes the basic point in the scientific activity. Currently most agent-oriented methodologies are supported by small teams of academic researchers, and as a result, most of them are in an early stage and still in the first context of mostly \academic approaches for agent-oriented systems development. Moreover, such methodologies are not well documented and very often defined and presented only by focusing on specific aspects of the methodology. The role played by meta- models becomes fundamental for comparing and evaluating the methodologies. In fact a meta-model specifies the concepts, rules and relationships used to define methodologies. Although it is possible to describe a methodology without an explicit meta-model, formalising the underpinning ideas of the methodology in question is valuable when checking its consistency or planning extensions or modifications. A good meta-model must address all the different aspects of a methodology, i.e. the process to be followed, the work products to be generated and those responsible for making all this happen. In turn, specifying the work products that must be developed implies dening the basic modelling building blocks from which they are built. As a building block, the agent abstraction alone is not enough to fully model all the aspects related to multi-agent systems in a natural way. In particular, different perspectives exist on the role that environment plays within agent systems: however, it is clear at least that all non-agent elements of a multi-agent system are typically considered to be part of the multi-agent system environment. The key role of environment as a first-class abstraction in the engineering of multi-agent system is today generally acknowledged in the multi-agent system community, so environment should be explicitly accounted for in the engineering of multi-agent system, working as a new design dimension for agent-oriented methodologies. At least two main ingredients shape the environment: environment abstractions - entities of the environment encapsulating some functions -, and topology abstractions - entities of environment that represent the (either logical or physical) spatial structure. In addition, the engineering of non-trivial multi-agent systems requires principles and mechanisms for supporting the management of the system representation complexity. These principles lead to the adoption of a multi-layered description, which could be used by designers to provide different levels of abstraction over multi-agent systems. The research in these fields has lead to the formulation of a new version of the SODA methodology where environment abstractions and layering principles are exploited for en- gineering multi-agent systems.

Search-based software engineering (SBSE) consists of the application of metaheuristic search techniques—such as genetic algorithms, simulated annealing, hill climbing, and tabu search—to software engineering problems. This idea is based on the observation that some software engineering activities can be thought of as optimization problems, and on the fact that techniques providing exact solutions are, very often, unable to adequately scale to large, real-world software engineering problems. Define fitness functions to guide an automated search for solutions, rather than attempting to construct solutions from scratch by hand. SBSE moves the engineer up the abstraction chain, from the tedious and expensive role of detailed solution construction, to the potentially more productive and more cost-effective area of fitness function design. This approach has been repeatedly demonstrated to have successful applications across the software engineering life cycle from requirements and project planning to maintenance and testing. This special issue focuses on applications of search SBSE to the maintenance phase of the life cycle. After software testing 2, software maintenance is the most widely studied application area for SBSE research. Work on SBSE applications to problems in software maintenance can be traced back to the pioneering work of the Mancoridis et al. 3, which led to the development of the Bunch tool for software re-modularization 4. In the past five years there has been a significant increase in SBSE applications to software maintenance and evolution. Search-based techniques have been applied to post-development code improvement problems, such as object-oriented refactoring 5, 6, and imperative language slicing and transformation 7. Other applications are related to software engineering economics and project management problems, such as release planning 8-12, effort, and cost estimation 13-16, and project staffing and planning 17-20. Last, but not the least, SBSE has been applied to deal with service-oriented architectures, for example for services QoS-aware composition 21, service-level agreement negotiation 22, and testing 23. A complete overview of SBSE with a discussion of future research trends can be found in the recent International Conference on Software Engineering (ICSE) ‘Future of Software Engineering’ paper 24. This issue of the Journal of Software Maintenance and Evolution: Research and Practice features three outstanding papers, dealing with different aspects of software maintenance and evolution: release planning and staffing, re-factoring, and component substitution. The paper ‘Optimized Staffing for Product Releases—Focused Search and its Application at Chartwell Technologies’ by Puneet Kapur, An Ngo-The, Günther Ruhe, and Andrew Smith presents an approach for optimizing the staffing for product releases. The problem they solve can be viewed as part of the more general release planning problem, which assigns features to releases such that technical, resource, risk, and budget constraints are met. In particular, the genetic search-based approach the authors propose aims at assigning human resources to software development tasks, taking them from a pool of developers having different levels of skills. The applicability of the proposed approach is demonstrated with an industrial case study. The paper ‘Search-Based Refactoring: an Empirical Study’ by Mark O'Keffe and Mel Ó Cinnéide presents an approach to automatically re-factor, using search-based techniques, object-oriented systems that undergo repeated addition of functionality. Such addition can, very often, deteriorate the quality of the system's underlying design. Their re-factoring is guided by the QMOOD quality model 25, and the paper compares different optimization techniques, such as Genetic Algorithms, Simulated Annealing, and Multiple Ascent Hill-Climbing. The results of experiments carried out on five software systems suggest that the latter technique outperforms the others. The paper ‘Search-based Many-to-one Component Substitution’ by Nicolas Desnos, Marianne Huchard, Guy Tremblay, Christelle Urtado, and Sylvain Vauttier presents a search-based approach that allows the replacement of an obsolete component with a composition of multiple components. When a component becomes obsolete, the approach searches for a minimal assembly of other components, exposing their functionality by means of ports that preserves the behavior of the original component. The proposed approach has been evaluated on a set of randomly generated components. We hope that readers will enjoy this special issue and find the selected papers insightful and inspiring. We would like to thank all the authors who presented papers for this special issue: there were many good quality papers, but unfortunately we could accept only three of them. Last, but not the least, we would like to thank the reviewers who provided timely, detailed and constructive feedbacks to the authors. Without their precious help this special issue would not have been possible.

work of W. Edwards Deming has convinced industry that must first measure quality and then emphasise process to improve quality. In response to Deming's arguments and in light of perception that software industry is unable to produce quality products on schedule, and within budget, more software development organisations are now emphasising process measurement, monitoring, and assessment. This paper describes what is meant by software process and discusses an approach for its measurement and improvement. INTRODUCTION Unreliable software makes big news, from emergency services disasters to social security payment blunders. Improved software quality is essential to ensure reliable products and services, and gain customer satisfaction. While software development has existed for more than four decades, we failed so far to make as an industry and as an engineering discipline rather than craft. Developing reliable and usable software that is delivered on and within budget still represents difficult endeavour for many organisations. As role of software becomes increasingly critical for businesses as well as for human lives, problems caused by software products that are late, over budget, or that do not work, become magnified. If lives are lost or people inconvenienced due to incapable computer software, news media is there to make big stories. Organisations are realising that their fundamental problems is immaturity of their software process. Robert Lai (1993) proposes that process improvement is second maturity wave of software industry. He states that the first wave of software was developed using waterfall model in 1970's. Today we are Transactions on Information and Communications Technologies vol 8, © 1994 WIT Press, www.witpress.com, ISSN 1743-3517 216 Software Management in midst of second wave maturity movement as we attempt to formally define development process and best ways to continuously improve it (Lai 1993). Taking lead in this area has been Watts Humphrey (Humphrey 1989, 1990, 1991), and Software Engineering Institute (SEI) at Carnegie Mellon University. SEI started in 1986 to develop process maturity framework to help organisations appraise maturity of their software process, and to provide guidance for organisations to improve their software process capability. A brief description of framework was released in September 1987, including maturity questionnaire. SEI evolved model and questionnaire into 5 -Level Capability Maturity Model (CMM) in 1991. In February 1993, released CMM version 1.1 (Paulk et al 1993). This paper discusses software process and describes capability maturity model (CMM). It also shows how CMM can be used as basis to measure maturity of software process of an organisation and plan its improvement. SOFTWARE QUALITY AND PROCESS IMPROVEMENT In his book Quality is Free Phil Crosby states that: Quality is free. It is not gift, is free. What cost money are unqualify of things all actions that involve not doing jobs right first time (Crosby 1980). If such statement is true for many disciplines, is particularly true for evidence is abundant in number of software products which exceeded their budget, were produced late, failed to satisfy user requirements, and are full of bugs. demand for improved software quality is increasing to ensure reliable products and services. benefits of improved quality comes in form of reduction in failure costs. For software projects failure costs include (DTI 1992): • costs of correcting defects, both before and after delivery • overruns against and budget • unnecessary high maintenance costs • indirect costs which users incur due to poor quality software link between process maturity and software quality is expressed in premise that The quality of software system is governed by quality of process used to develop and maintain it. One stumbling block to improving software quality seems to be that not enough attention is paid to overall development process itself. While software professionals typically devote their to developing, testing or documenting software products, no one has prime responsibility for improving software process. Experience has shown that if Transactions on Information and Communications Technologies vol 8, © 1994 WIT Press, www.witpress.com, ISSN 1743-3517 Managing Systems 217 no one is working on software process, orderly improvement is unlikely. process certainly won't improve itself, rather, most likely, will deteriorate over time. Continuous improvement can occur only if process infrastructure is in place. Watts Humphrey argues in an early article published in Datamation, April 1989 that: Without work on process, there will be little or no progress in improving software. THE SOFTWARE PROCESS Adopting process view of software development represents revolutionary change in perspective. A process orientation to software development involves elements of structure, focus, measurement, ownership, skills, and supporting technology. In this section we investigate what is meant by software process. What Is Software Process According to Webster's dictionary, process is a system of operations in producing something .. series of actions, changes, or functions that achieve an end result. Chambers Concise Dictionary defines process as a series of actions or events .. sequence of operations or changes undergone. IEEE defines process as a sequence of steps performed for given purpose. In general business context, process is defined as a structured, measured set of activities designed to produce specified output for particular customer or market (Davenport 1993). These definitions put strong emphasis on HOW work is done, in contrast to product focus's emphasis on WHAT. Accordingly, process can be considered as a specific ordering of work activities across and place, with beginning, an end, and clearly identified inputs and outputs: structure for action. In this paper we will adopt following definitions quoted in (Humphrey 1990, Paulk et al 1993) are intended to encompass software throughout its life, which covers new development, enhancement, and repair. software process is The set of activities, methods, and practices used in production and evolution of software. software engineering process is The total set of software engineering activities needed to transform user's requirements into software. Transactions on Information and Communications Technologies vol 8, © 1994 WIT Press, www.witpress.com, ISSN 1743-3517 218 Software Management People and Software Process Software development is still people-intensive activity. Talented people are most important element in any software organisation. Even if you get best people available, if they do not follow common process, if everyone wrote in different programming languages, used different conventions, or didn't co-ordinate their design and code changes with their peers, results will be chaos. Successful software organisations have learned that even best professionals need structured and disciplined environment in which to do cooperative work. Software organisations that do not establish such disciplines condemn their people to endless hours of repetitively solving technically trivial problems. obvious fact is that attracting best people is vital, but is also essential to support them with an effectively managed software process. Technology and Software Process Another myth is widespread belief that some technologically advanced tool or method will provide magic answer to software crisis. This is not only wrong, but is dangerous. Organisations which jumped on bandwagon of CASE tools, and ended up in failure and wasted and effort have learned their lesson hard way. Just ask yourself before introducing technology: What do I want to automate? In absence of defined, practised, and managed process, introducing automation can lead to increased chaos. There are several factors which limit effective use of software technology: an illdefined process, inconsistent process implementation, and poor process management. Software technology cannot be fully effective until these problems have been properly addressed. Need for Defined Process If no effort is made to define and enhance software development process across whole organisation, each software development project latches on to its own tools, methods, and practices with little guidance available on how to use them. This ad hoc approach will not be sufficient to tackle task of developing complex software systems. goal of software process management is to enable organisations to produce software that meets cost, schedules, and quality objectives. principles are same that underpins statistical process control-principles that have been successfully used in controlling scientific experiments and in high -volume manufacturing operations. Statistical concepts have been found to be just as applicable to software development as they are to production of manufactured goods such as motor cars. Applying such concepts will only be possible if there is defined and managed process. Software Process Models Transactions on Information and Communications Technologies vol 8, © 1994 WIT Press, www.witpress.com, ISSN 1743-3517

Despite preceding related publications, works dealing with the resolution of software engineering problems by search techniques has especially risen since 2001. By its first decade, the Search Based Software Engineering (SBSE) approach has been successfully employed in several software engineering contexts, using various optimization techniques. Aside the relevance of such applications, knowledge regarding the publication patterns on the field plays an important role to its understanding and identity. Such information may also shed light into SBSE trends and future. This paper presents the first bibliometric analysis to SBSE publications. The study covered 740 publications of the SBSE community from 2001 through 2010. The performed bibliometric analysis concerned mainly in four categories: Publication, Sources, Authorship, and Collaboration. Additionally, estimates for the next years of several publication metrics are given. The study also analyzed the applicability of bibliometric laws in SBSE, such as Bradfords and Lotka.

This challenges and open problems paper discusses the role of software engineers when combining modeling with Search-Based Software Engineering (SBSE). With SBSE techniques increasingly providing insights into difficult software modeling problems, the role of the software engineer might be thought of as diminishing. However, given the crucial role of the model understanding in SBSE, we argue that the role of the software engineer will become more important through interactive SBSE, although exploiting this interaction remains a challenge for the field.

Multi-agent systems have received much attention in recent years because of their many advantages in complex and distributed environments. There are a number of methodologies have been proposed for multi- agent engineering process such as Multi-agent System Engineering (MaSE). I have used the MaSE engineering process for the development of my ontology based multi- agent system for the Academic Institute. In the era of Semantic Web, the ontology has established as a powerful tool to enable knowledge sharing and it is an important means in Semantic Web to achieve the semantic interoperability among heterogeneous distributed systems. Both ontology and agent technologies are central to the semantic web, and their combined use will enable the sharing of heterogeneous, autonomous knowledge sources in a capable, adaptable and extensible manner. Ontology is used throughout the multi-agent system to assist the interactions among different agents as well as to improve the quality of the service provided by each agent. This paper focuses on the utilization of combining both Ontology and Multi-Agent System (MAS) structure towards system integration for University teaching environment. In this paper, I include system prototype for ontology based multi-agent system. I have used Web Ontology Language (OWL) for the development of domain ontology for the Academic Institute and KQML as an agent communication language. Finally I have developed ontology-based multi- agent system for the university teaching environment by taking the benefits of both renowned technologies.

This special issue of Empirical Software Engineering consists of revised and extended versions of five selected papers originally presented at the 1st International Symposium on Search Based Software Engineering (SSBSE 2009). The symposium was held at Cumberland Lodge, Windsor, UK, in May 2009, and brought together the academic researchers and industrial practitioners that make up the international Search Based Software Engineering (SBSE) community for the first conference dedicated to the subject. Search Based Software Engineering is the application of search-based optimization techniques—for example, genetic algorithms, hill climbing, simulated annealing, taboo search, and ant colony optimization—to the solution of software engineering problems. Many such problems are often difficult or impossible to solve manually, and moreover, as modern software becomes more complex and adopts distributed and highly parallel architectures, are proving to be increasingly intractable using traditional automated techniques. In contrast, SBSE approaches can leverage sophisticated optimisation algorithms running on affordable computing infrastructure to solve these problems. SBSE approaches also demonstrate other advantages: they can be used to find acceptably good solutions when time, or resources, are limited, and—by utilizing multi-objective search algorithms—can derive a range of solutions that illustrate potential trade-offs between competing goals. For these reasons, both academic and industrial interest in SBSE is increasing rapidly. A recent survey,1 undertaken by the UK-based SEBASE project, indicated an exponential growth of

Self-adaptation ability is particularly desirable for mission critical software (MCS). This paper proposes a fuzzy control-based approach to provide a systematic, engineering, and intuitive way for programmers to achieve software self adaptation. This approach uses fuzzy logic to handle uncertainty in software, uses natural-language style to describe self-adaptation logic, and makes control visible in software. These greatly shorten the knowledge and semantics gap between control engineering and software engineering, and reduce the realization difficulty of software self-adaptation. We illustrate our approach through the development of an adaptive MCS application in process control systems.

Welcome to the International Conference on Software and Systems Process (ICSSP) held in Zurich, Switzerland, on June 2nd and 3rd, 2012. Having grown from a Software Process Workshop in 2005 to the International Conference on Software Process (ICSP) in 2007, and finally to the International Conference on Software and Systems Process (ICSSP) in 2011, this conference series has established its place in the software engineering community as a respected conference focusing on research and practice related to software and systems development process. Software and software systems development use processes as one major means for achieving and ensuring the quality of the final product. This leads to specific challenges regarding software processes such as simplicity, adaptability, standard conformance, the coordination of multiple domains and lifecycles, etc. Processes from other domains (e.g., health care, manufacturing, business, aerospace, automotive systems, and others) face similar challenges. Yet, they share interesting similarities with and differences to software processes. The question is: How can we in the software domain learn from other domains to meet our software process engineering challenges? The ICSSP conferences hence connect the traditional software and systems process community with outside views and insights, broadening its scope of software development processes to system development and explicitly including processes of other domains such as health care, business, and manufacturing. By sharing process development theories and practices from such domains, ICSSP 2012 is investigating novel solutions to today's process challenges. Correspondingly, the main theme of ICSSP 2012 is Beyond Software and Software Systems Processes, signaling our goal of learning from domains other than the traditional software process community.

This article attempts to address the issues of evaluating self-adaptive software systems, an emerging discipline. Since the field is in its early phase and has not produced enough mature systems for consideration, we try to approach the evaluation issue by considering a descriptive model of selfadaptive software based on control systems. Taking inspirations and using the vocabularies from the feedforward and feedback control paradigms, this article will illustrate the analogous properties in self-adaptive software and its evaluation consideration such as stability and robustness. Existing approaches to self-adaptive software take different aspects, ranging from viewing it as new programming paradigm, new architecture style, new modeling paradigm to a new software engineering principle. This article tries to elicit the evaluation consideration from these different aspects.KeywordsEvaluation MetricsArchitecture StyleProgramming ParadigmModeling ParadigmRuntime BehaviorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Software testing is a fundamental step in the software development lifecycle. Its purpose is to evaluate the quality of software applications. Regression testing is an important testing methodology in software testing. The purpose of regression testing is to validate the software after each change of its code. This involves adding new test cases to the test suite and running the test suite as the software changes, making the test suite larger. The cost and time of the project are affected by the test suite size. The challenge is to run regression testing with a smaller number of test cases and larger amount of software coverage. Minimization of the test suite with maximization of the software coverage is an NP-complete problem. Search-based software engineering is an important topic in software engineering, which addresses software engineering optimization problems to find the optimal/approximate solution of the given problem. This study investigated an approach to reducing the regression testing effort and saving time. It also solved the regression testing optimization problem by achieving the maximum test suite coverage with the minimum test suite size. Several experiments were conducted to obtain the optimal solutions for the regression testing problem. We propose an optimization methodology that combines a genetic algorithm and a greedy algorithm to optimize regression testing by respectively maximizing the software test coverage and minimizing the test suite size. The proposed methodology can conveniently deliver fault-free, fully covered, and powerful programs for mission-critical functions. It can be applied to test a real-time system that has high requirements for reliability, security, and safety.

Software engineers have always been experimenting with various models in an endeavor to improve upon software processes. Recently, a new area of software process improvement through experience management has got attention of the software engineering community. Software developers are experimenting with experience management to improve software process. This chapter explores the role of experience management in mitigating the effects of software crisis and investigates the current state of EM in Indian software engineering environment. The chapter discusses the need and integration of knowledge and experience management practices in software engineering environments. The chapter begins with the discussion of the ills of software engineering, classically referred to as “software crisis,” like late delivery of the systems, ill-functionality, and cost overruns, and justifies how managing the knowledge and experience of software engineers and practitioners can ward off these evils. The chapter then discusses the models and methodologies for knowledge and experience management in software engineering. Finally, the chapter sheds light on the major works carried out in this area and suggests possible future research avenues in this domain.

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

Many software engineering problems are multi-objective in nature, which has been largely recognized by the Search-based Software Engineering (SBSE) community. In this regard, Pareto- based search algorithms, e.g., Non-dominated Sorting Genetic Algorithm II, have already shown good performance for solving multi-objective optimization problems. These algorithms produce Pareto fronts, where each Pareto front consists of a set of non- dominated solutions. Eventually, a user selects one or more of the solutions from a Pareto front for their specific problems. A key challenge of applying Pareto-based search algorithms is to select appropriate quality indicators, e.g., hypervolume, to assess the quality of Pareto fronts. Based on the results of an extended literature review, we found that the current literature and practice in SBSE lacks a practical guide for selecting quality indicators despite a large number of published SBSE works. In this direction, the paper presents a practical guide for the SBSE community to select quality indicators for assessing Pareto-based search algorithms in different software engineering contexts. The practical guide is derived from the following complementary theoretical and empirical methods: 1) key theoretical foundations of quality indicators; 2) evidence from an extended literature review; and 3) evidence collected from an extensive experiment that was conducted to evaluate eight quality indicators from four different categories with six Pareto-based search algorithms using three real industrial problems from two diverse domains.

In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article 1 provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.