Tinny Ng

An Exploratory Study on the Relation between User Interface Complexity and the Perceived Quality

The number of mobile applications has increased drastically in the past few years. Some applications are superior to the others in terms of user-perceived quality. User-perceived quality can be defined as the user’s opinion of a product. For mobile applications, it can be quantified by the number of downloads and ratings. Earlier studies suggested that user interface (UI) barriers (i.e., input or output challenges) can affect the user-perceived quality of mobile applications. In this paper, we explore the relation between UI complexity and user-perceived quality in Android applications. Furthermore, we strive to provide guidelines for the proper amount of UI complexity that helps an application achieve high user-perceived quality through an empirical study on 1,292 mobile applications in 8 different categories.

Automatic Propagation of User Inputs in Service Composition for End-Users

End-users conduct various on-line activities. Quite often they re-visit websites and use services to perform repeated activities, such as on-line shopping. The end-users are required to enter the same information into various web services to accomplish such repeated tasks. Typing redundant information repetitively into such services negatively impacts the user experience. In this study, we propose an approach to prevent end-users from such unnecessary interruption. Our approach propagates user inputs across services by linking similar input and output parameters. Our approach also pre-fills values to the input parameters which could not be filled by the values from other input or output parameters. We propose a meta-data model for storing user inputs and an Input Parameter Context Model for identifying similar input or output parameters. We have implemented our approach and evaluated it on the real world services through an empirical study. Our overall approach can reduce on average 37% of input parameters through the execution of composed services.

Towards Quality of Experience Driven Service Composition

Web service composition enables seamless and dynamic integration of applications on the web. Generally a user has to find services, select proper services and form a flow to create a service composition. The performance of the composed application is determined by the performance of the involved Web services. Current work in web service selection and discovery are based on non-functional, quality of service (QoS) aspects (such as, response time and availability). However, QoS information does not reflect an end users perspective on the quality of services. An end users perspective of a service is a credible source of information, covers diverse platforms and geographical locations. In this position paper, we provide an approach to extract a users perception of the quality of services from user reviews on services and use such information to compose services. We provide a mechanism to select a particular service from a pool of services and recommend the best service execution path in a composite service.

Automatic Reuse of User Inputs to Services among End-Users in Service Composition

End-users conduct various on-line activities. Quite often, they re-visit websites and use services to perform re-occurring activities, such as on-line shopping. The end-users are required to enter the same information into various web services to accomplish such re-occurring tasks. It can negatively impact user experience when a user needs to type the re-occurring information repetitively into such web services. In this paper, we propose an approach to prevent end-users from performing such repetitive tasks. Our approach propagates user inputs across services by linking similar input and output parameters. Our approach pre-fills values to the input parameters for an end-user using his or her previous inputs. To increase the chance of identifying a proper value for an input parameter performed by one end-user, our approach also leverages the inputs from other end-users. We identify and link similar end-users to enable the propagation of user inputs among end-users. We have designed and developed a prototype. We also conduct an empirical study to evaluate our approach using the real world services. The empirical results show that our approach using an end-users previous inputs can reduce on average 41 percent of repetitive typing for the execution of composed services. Furthermore, the previous inputs from the similar end-users can improve our approach in reducing the repetitive typing for an end-user.

Learning to Reuse User Inputs in Service Composition

Users visit web services and compose them to accomplish on-line tasks. Normally, users enter the same information into various web services to finish such tasks. However, repetitively typing the same information into services is unnecessary and decreases the service composition efficiency. In this paper, we propose a context-aware ranking approach to recommend previous user inputs into input parameters and save users from repetitive typing. We develop five different ranking features constructed from various types of information, such as user contexts. We adopt a learning-to-rank approach, a machine learning technology automatically constructing the ranking model, and integrate our ranking features into a state-of-the-art learning-to-rank framework. Our approach learns the information of interactions between input parameters and user inputs to reuse user inputs under different contexts. Through an empirical study on 960 real services, our approach outperforms two baseline approaches on ranking values to input parameters of composed services. Moreover, we observe that textual information affects the ranking most and the contextual information of location matters the most to ranking among various types of contextual data.

Context-aware Service Input Ranking by Learning from Historical Information

Users visit on-line services and compose them to accomplish on-line tasks, such as shopping on-line. Quite often, users enter the same information into various on-line services to finish on-line tasks. However, repetitively typing the same information into web forms is a tedious job for users. In this paper, we propose a context-aware ranking framework to rank values for input parameters. We propose 6 categories of ranking features constructed from various types of information, such as user contexts and patterns of user inputs. Our framework adopts learning-to-rank (LtR) algorithms that consist of a set of machine learned models to automatically construct ranking models by integrating the ranking features. When a user enters a value to an input parameter, an interaction between the user input and the input parameter is established. Our framework learns information relevant to such interactions and ranks user inputs in different contexts. Through empirical studies on the real-world on-line services, we obtain the following main results: (1) Among the 8 state-of-the-art learning-to-rank models, RankBoost can outperform other LtR models on ranking user inputs for input parameters; (2) Our framework using IRSVM that performs the worst among the LtR models outperforms the two baseline conventional ranking models and Google Chrome Autofilling, an industrial tool, on ranking user inputs to input parameters; and (3) We observe that the textual information of user inputs and input parameters is the most influential factor on ranking user inputs. Among the various types of contextual data, user locations and time matter the most to the ranking of user inputs.

Automatic Filling Values to Services: A Road Map

It is common for users to explicitly or implicitly compose on-line services to accomplish daily tasks, such as shopping for a pair of shoes on-line. However, unnecessary and repetitive data typing into the services would negatively impact the user experience and decrease the efficiency of service composition. Recent studies have proposed several approaches to help users fill in values to services automatically. However, existing approaches suffer the following two drawbacks: 1) poor accuracy of filling values to services, 2) not designed for service composition. In this position paper, we first present the recent approaches improving the process of filling values to services. We then present our recent achievements and results of using our proposed approaches to help users fill values to services. Lastly, we discuss the various opportunities and challenges in the research field of filling values to services.