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Non-functional requirements (NFRs) address important issues in software systems, and are vital in successful software. The NFR problems in a system reflect the complexity of that system. Ideally, NFRs are systematically investigated to determine the aspects that may be harmonious or cause conflicts so that conflicts should be detected as early as possible. The work aims to extend the scopes of the NFRs framework and softgoal interdependency graph (SIG) for modelling and analysing NFRs. A formal ontological approach to modelling NFR interactions is proposed, and a core NFR ontology is developed in the ontology web language. The mechanisms involved in NFR interactions with SIG models (through interdependencies in the softgoals) are investigated and formalised. The NFRs are first analyses using a semantic modelling process to provide evaluation criteria. Next, correlation rules for implicit softgoal interdependencies are proposed from knowledge of the domain, and rules for indirect semantic correlations are proposed by extending the delta evaluation method. Reasonable rules in the semantic web environment are defined to allow conflicts between NFRs in SIG instance models to be detected and analysed. A concepts proof is performed to illustrate tool support of the proposed method, and validate the rules for conflict detection.

In recent years, the number of online services has grown rapidly, invoking the required services through the cloud platform has become the primary trend. How to help users choose and recommend high-quality services among huge amounts of unused services has become a hot issue in research. Among the existing QoS prediction methods, the collaborative filtering (CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. Although existing deep learning methods could capture high-dimensional nonlinear features better, most of them only use the single feature of identity, and the problem of network deepening gradient disappearance is serious, so the effect of QoS prediction is unsatisfactory. To address these problems, we propose an advanced probability distribution and location-aware ResNet approach for QoS Prediction (PLRes). This approach considers the historical invocations probability distribution and location characteristics of users and services, and first uses the ResNet in QoS prediction to reuses the features, which alleviates the problems of gradient disappearance and model degradation. A series of experiments are conducted on a real-world web service dataset WS-DREAM. At the density of 5%–30%, the experimental results on both QoS attribute response time and throughput indicate that PLRes performs better than the existing five state-of-the-art QoS prediction approaches.

In social networks, the personal attributes or hobbies of the users are exposed to the server to establish the relationships. Service providers may store these information for commercial purpose or statistical analysis. Furthermore, the server may expose to external attacks, which may disclose users' privacy information. In this paper, we present a hierarchical blockchain-based attribute matching scheme, which realizes privacy-preserving attribute matching under multiple semi-trusted servers. The scheme employs CP-ABE and bloom filter to satisfy the requirements of the users to make friend discovery, and reduces the computation cost of users by outsourcing decryption of CP-ABE. Besides, the hierarchical blockchain only implements the consensus and storage of matching results on the blockchain, while the complex calculations and a large amount of data storage are off-chain, which reduces the consumption of the blockchain and improves the operation efficiency. Finally, we prove the scheme can resist single point failure, collusion attack, internal attack and external attack, the experimental results demonstrate the proposed scheme is feasibility and efficiency.