Requirements model driven adaption and evolution of Internetware

Today’s software systems need to support complex business operations and processes. The development of the web-based software systems has been pushing up the limits of traditional software engineering methodologies and technologies as they are required to be used and updated almost real-time, so that users can interact and share the same applications over the internet as needed. These applications have to adapt quickly to the diversified and dynamic changing requirements in the physical, technological, economical and social environments. As a consequence, we are expecting a major paradigm shift in software engineering to reflect such changes in computing environment in order to better address the fundamental needs of organisations in this new era. Existing software technologies, such as model driven development, business process engineering, online (re-)configuration, composition and adaptation of managerial functionalities are being repurposed to reduce the time taken for software development by reusing software codes. The ability to dynamically combine contents from numerous web sites and local resources, and the ability to instantly publish services worldwide have opened up entirely new possibilities for software development. In retrospect to the ten years applied research on Internetware, we have witnessed such a paradigm shift, which brings about many changes to the developmental experience of conventional web applications. Several related technologies, such as cloud computing, service computing, cyber-physical systems and social computing, have converged to address this emerging issue with emphasis on different aspects. In this paper, we first outline the requirements that the Internetware software paradigm should meet to excel at web application adaptation; we then propose a requirement model driven method for adaptive and evolutionary applications; and we report our experiences and case studies of applying it to an enterprise information system. Our goal is to provide high-level guidelines to researchers and practitioners to meet the challenges of building adaptive industrial-strength applications with the spectrum of processes, techniques and facilities provided within the Internetware paradigm

RULE BASED ADAPTATION: LITERATURE REVIEW

Rule based adaptive systems are growing in popularity and rules have been considered as an effective and elastic way to adapt systems. A rule based approach allows transparent monitoring of performed adaptation actions and gives an important advantage of easily modifiable adaptation process. The goal of this paper is to summarize literature review on rule based adaptation systems. The emphasis is put on rule types, semantics used for defining rules and measurement of effectiveness and correctness of rule based adaptation systems. The literature review has been done following a systematic approach consisting of three steps: planning, reviewing and analysis. Targeted research questions have been used to guide the review process. The review results are to be used for conducting further research in the area of rule based context-aware adaptive systems. This paper accents the potential of using rules as means to perform adaptive actions in enterprise applications taking into account contextual factors as well as points challenges, difficulties and open issues for planning, developing, implementing and running of such systems

An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.Comment: 8 pages, 5 figure

A Reengineering Approach to Reconciling Requirements and Implementation for Context - Aware Web Services Systems

In modern software development, the gap between software requirements and implementation is not always conciliated. Typically, for Web services-based context-aware systems, reconciling this gap is even harder. The aim of this research is to explore how software reengineering can facilitate the reconciliation between requirements and implementation for the said systems. The underlying research in this thesis comprises the following three components. Firstly, the requirements recovery framework underpins the requirements elicitation approach on the proposed reengineering framework. This approach consists of three stages: 1) Hypothesis generation, where a list of hypothesis source code information is generated; 2) Segmentation, where the hypothesis list is grouped into segments; 3) Concept binding, where the segments turn into a list of concept bindings linking regions of source code. Secondly, the derived viewpoints-based context-aware service requirements model is proposed to fully discover constraints, and the requirements evolution model is developed to maintain and specify the requirements evolution process for supporting context-aware services evolution. Finally, inspired by context-oriented programming concepts and approaches, ContXFS is implemented as a COP-inspired conceptual library in F#, which enables developers to facilitate dynamic context adaption. This library along with context-aware requirements analyses mitigate the development of the said systems to a great extent, which in turn, achieves reconciliation between requirements and implementation

AntFuzzer: A Grey-Box Fuzzing Framework for EOSIO Smart Contracts

In the past few years, several attacks against the vulnerabilities of EOSIO smart contracts have caused severe financial losses to this prevalent blockchain platform. As a lightweight test-generation approach, grey-box fuzzing can open up the possibility of improving the security of EOSIO smart contracts. However, developing a practical grey-box fuzzer for EOSIO smart contracts from scratch is time-consuming and requires a deep understanding of EOSIO internals. In this work, we proposed AntFuzzer, the first highly extensible grey-box fuzzing framework for EOSIO smart contracts. AntFuzzer implements a novel approach that interfaces AFL to conduct AFL-style grey-box fuzzing on EOSIO smart contracts. Compared to black-box fuzzing tools, AntFuzzer can effectively trigger those hard-to-cover branches. It achieved an improvement in code coverage on 37.5% of smart contracts in our benchmark dataset. AntFuzzer provides unified interfaces for users to easily develop new detection plugins for continually emerging vulnerabilities. We have implemented 6 detection plugins on AntFuzzer to detect major vulnerabilities of EOSIO smart contracts. In our large-scale fuzzing experiments on 4,616 real-world smart contracts, AntFuzzer successfully detected 741 vulnerabilities. The results demonstrate the effectiveness and efficiency of AntFuzzer and our detection p