Process Mining is a famous technique which is frequently applied to Software Development Processes, while being neglected in Human-Computer Interaction (HCI) recommendation applications. Organizations usually train employees to interact with required IT systems. Often, employees, or users in general, develop their own strategies for solving repetitive tasks and processes. However, organizations find it hard to detect whether employees interact efficiently with IT systems or not. Hence, we have developed a method which detects inefficient behavior assuming that at least one optimal HCI strategy is known. This method provides recommendations to gradually adapt users' behavior towards the optimal way of interaction considering satisfaction of users. Based on users' behavior logs tracked by a Java application suitable for multi-application and multi-instance environments, we demonstrate the applicability for a specific task in a common Windows environment utilizing realistic simulated behaviors of users.
Formal methods, such as Process Mining techniques, have been used for usability analysis and user assistance for a long time to achieve effective, efficient and satisfying UIs. However, they have not been applied to provide automatic HCI recommendations. For example, proposed a method called AUGUR to assist users in navigating and entering data in form applications @cite_8 . However, this method applies only to web-based applications and does not consider the interaction between multiple applications.
@cite_8
1987533592 949909655 2338918436 2014577207
As user interfaces become more and more complex and feature laden, usability tends to decrease. One possibility to counter this effect are intelligent support mechanisms. In this paper, we present AUGUR, a system that provides context-aware interaction support for navigating and entering data in arbitrary form-based web applications. We further report the results of an initial user study we performed to evaluate the usability of such context-aware interaction support. AUGUR combines several novel approaches: (i) it considers various context sources for providing interaction support, and (ii) it contains a context store that mimics the user's short-term memory to keep track of the context information that currently influences the user's interactions. AUGUR thereby combines the advantages of the three main approaches for supporting the user's interactions, i.e. knowledge-based systems, learning agents, and end-user programming. Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lion's share of process mining research has been focusing on process discovery (creating process models from raw data) and replay techniques to check conformance and analyze bottlenecks. These techniques have helped organizations to address compliance and performance problems. However, for a more refined analysis, it is essential to correlate different process characteristics. For example, do deviations from the normative process cause additional delays and costs? Are rejected cases handled differently in the initial phases of the process? What is the influence of a doctor's experience on treatment process? These and other questions may involve process characteristics related to different perspectives (control-flow, data-flow, time, organization, cost, compliance, etc.). Specific questions (e.g., predicting the remaining processing time) have been investigated before, but a generic approach was missing thus far. The proposed framework unifies a number of approaches for correlation analysis proposed in literature, proposing a general solution that can perform those analyses and many more. The approach has been implemented in ProM and combines process and data mining techniques. In this paper, we also demonstrate the applicability using a case study conducted with the UWV (Employee Insurance Agency), one of the largest "administrative factories" in The Netherlands. Recent years have witnessed the increasing emphasis on human aspects in software engineering research and practices. Our survey of existing studies on human aspects in software engineering shows that screen-captured videos have been widely used to record developers' behavior and study software engineering practices. The screen-captured videos provide direct information about which software tools the developers interact with and which content they access or generate during the task. Such Human-Computer Interaction (HCI) data can help researchers and practitioners understand and improve software engineering practices from human perspective. However, extracting time-series HCI data from screen-captured task videos requires manual transcribing and coding of videos, which is tedious and error-prone. In this paper we report a formative study to understand the challenges in manually transcribing screen-captured videos into time-series HCI data. We then present a computer-vision based video scraping technique to automatically extract time-series HCI data from screen-captured videos. We also present a case study of our scvRipper tool that implements the video scraping technique using 29-hours of task videos of 20 developers in two development tasks. The case study not only evaluates the runtime performance and robustness of the tool, but also performs a detailed quantitative analysis of the tool's ability to extract time-series HCI data from screen-captured task videos. We also study the developer's micro-level behavior patterns in software development from the quantitative analysis. During software development, a developer often needs to discover specific usage patterns of Application Programming Interface (API) methods. However, these usage patterns are often not well documented. To help developers to get such usage patterns, there are approaches proposed to mine client code of the API methods. However, they lack metrics to measure the quality of the mined usage patterns, and the API usage patterns mined by the existing approaches tend to be many and redundant, posing significant barriers for being practical adoption. To address these issues, in this paper, we propose two quality metrics (succinctness and coverage) for mined usage patterns, and further propose a novel approach called Usage Pattern Miner (UP-Miner) that mines succinct and high-coverage usage patterns of API methods from source code. We have evaluated our approach on a large-scale Microsoft codebase. The results show that our approach is effective and outperforms an existing representative approach MAPO. The user studies conducted with Microsoft developers confirm the usefulness of the proposed approach in practice.