Users increasingly depend on mobile applications to get access to software services, social networks, and physical devices. When using mobile applications, users often want to achieve personal goals rather than merely perform individual tasks. To achieve a goal, a user often needs to combine software services, social cooperation, and possibly manual work. Moreover, a goal can often be achieved in different ways, each of which involves an alternative sequence of tasks. Accordingly, mobile applications should be customizable, to accommodate user preferences, and adaptive in changing their configuration automatically if the current configuration is failing. In this paper, we propose an improved runtime goal model that can manage runtime lifecycle of goals and adaptively schedule the activation of goals and execution of tasks. Based on the model, we propose an agent-based framework called MobiGoal, which combines software services, social cooperation, and manual work for achieving user goals and provides an infrastructure for developing customizable and adaptive mobile applications for personal goals. We have developed an implementation for Android platform and conducted an empirical study. The results show that MobiGoal applications can effectively support users to adaptively achieve their goals and MobiGoal can significantly save effort of application development for specific goals.

In Online-to-Offline (O2O) commerce, customer services may need to be composed from online and offline services. Such composition is challenging, as it requires effective selection of appropriate services that, in turn, support optimal combination of both online and offline services. In this paper, we address this challenge by proposing an approach to O2O service composition which combines offline route planning and social collaboration to optimize service selection. We frame general O2O service composition problems using timed automata and propose an optimization procedure that incorporates: (1) a Markov Chain Monte Carlo (MCMC) algorithm to stochastically select a concrete composite service, and (2) a model checking approach to searching for an optimal collaboration plan with the lowest cost given certain time constraint. Our procedure has been evaluated using the simulation of a rich scenario on effectiveness and scalability.

Internetware is required to respond quickly to emergent user requirements or requirements changes by providing application upgrade or making context-aware recommendations. As user requirements in Internet computing environment are often changing fast and new requirements emerge more and more in a creative way, traditional requirements engineering approaches based on requirements elicitation and analysis cannot ensure the quick response of Internetware. In this paper, we propose an approach for mining context-aware user requirements from crowd contributed mobile data. The approach captures behavior records contributed by a crowd of mobile users and automatically mines context-aware user behavior patterns (i.e., when, where and under what conditions users require a specific service) from them using Apriori-M algorithm. Based on the mined user behaviors, emergent requirements or requirements changes can be inferred from the mined user behavior patterns and solutions that satisfy the requirements can be recommended to users. To evaluate the proposed approach, we conduct an experimental study and show the effectiveness of the requirements mining approach.

Requirements-driven approaches provide an effective mechanism for self-adaptive systems by reasoning over their runtime requirements models to make adaptation decisions. However, such approaches usually assume that the relations among alternative behaviours, environmental parameters and requirements are clearly understood, which is often simply not true. Moreover, they do not consider the influence of the current behaviour of an executing system on adaptation decisions. In this paper, we propose an improved requirements-driven self-adaptation approach that combines goal reasoning and case-based reasoning. In the approach, past experiences of successful adaptations are retained as adaptation cases, which are described by not only requirements violations and contexts, but also currently deployed behaviours. The approach does not depend on a set of original adaptation cases, but employs goal reasoning to provide adaptation solutions when no similar cases are available. And case-based reasoning is used to provide more precise adaptation decisions that better reflect the complex relations among requirements violations, contexts, and current behaviours by utilizing past experiences. Our experimental study with an online shopping benchmark shows that our approach outperforms both requirements-driven approach and case-based reasoning approach in terms of adaptation effectiveness and overall quality of the system.

Software systems contain many implicit application-specific business and programming rules. These rules represent high-level logical structures and processes for application-specific business and programming concerns. They are crucial for program understanding, consistent evolution, and systematic reuse. However, existing pattern mining and analysis approaches cannot effectively mine such application-specific rules. In this paper, we present an approach for mining logical clones in software that reveal high-level business and programming rules. Our approach extracts a program model from source code, and enriches the program model with code clone information, functional clusters (i.e., a set of methods dealing with similar topics or concerns), and abstract entity classes (representing sibling entity classes). It then analyzes the enriched program model for mining recurring logical structures as logical clones. We have implemented our approach in a tool called MiLoCo (Mining Logical Clone) and conducted a case study with an open-source ERP and CRM software. Our results show that MiLoCo can identify meaningful and useful logical clones for program understanding, evolution and reuse.