Aditya Ghose

Robotic Process Automation (RPA) design and implementation requires an architecture which facilitates the seamless transition between human agents, robotic agents, and intelligent agents to automate information acquisition tasks and decision-making tasks. Coordination of those agents must consider various factors, such as efficiency of a resource when completing tasks, the quality of completed complex tasks, and the cost of the used resources. This paper proposes a novel approach for generating an optimal architecture based on distinct types of resources, including human agents, intelligent agents, and robotic agents. An optimal architecture is the optimal enactment of process instances executed by a combination of human and automation agents based on their characteristics. The architecture provides a set of resources and their characteristics that are tailored to meet multiple objectives for process execution. The proposed approach is validated through an empirical evaluation based on a real-world business process. An empirical evaluation demonstrates that, given equal computational time, our approach outperforms conventional constraint optimization ILOG CPLEX [1].

Aligning strategic objectives with business services is important for an organisation's long-term success. The automation of those alignments offers significant benefits in terms of identifying strategies that are not supported by any services, or services that are irrelevant to the organisation's strategies. Automated strategy-service alignment also enables organizations to proactively respond to changes in complex environments and allocate resources effectively to increase the likelihood of achieving strategic objectives. StraAlgin offers a methodology and tool for defining, modeling and refining business objectives and strategies, and aligning them with business services. It also enables automated identification of the optimal choice of services that could realize organizational strategies. Evaluation results have demonstrated a high level accuracy of our approach and its scalability to a large number of strategies and services. This paper proposes an approach to automate the assessment and alignment of services and strategies. Our approach is implemented in the form of StraAlign toolkit. StraAlign provides the necessary vocabulary for business objective description and offers automated support to improve the evaluation and implementation of strategic service alignments.

Everyone acknowledges the importance of responsible computing, but practical advice is hard to come by. Important Internet applications are ways to accomplish business processes. We investigate how they can be geared to support responsibility as illustrated via sustainability. Sustainability is not only urgent and essential but also challenging due to engagement with human and societal concerns, diverse success criteria, and extended temporal and spatial scopes. This article introduces a new framework for developing responsible Internet applications that synthesizes the perspectives of the theory of change, participatory system mapping, and computational sociotechnical systems.

Automated Program Repair has attracted significant research in recent years, leading to diverse techniques that focus on two main directions: search-based and semantic-based program repair. The former techniques often face challenges due to the vast search space, resulting in difficulties in identifying correct solutions, while the latter approaches are constrained by the capabilities of the underlying semantic analyser, limiting their scalability. In this paper, we propose NEVERMORE, a novel learning-based mechanism inspired by the adversarial nature of bugs and fixes. NEVERMORE is built upon the Generative Adversarial Networks architecture and trained on historical bug fixes to generate repairs that closely mimic human-produced fixes. Our empirical evaluation on 500 real-world bugs demonstrates the effectiveness of NEVERMORE in bug-fixing, generating repairs that match human fixes for 21.2% of the examined bugs. Moreover, we evaluate NEVERMORE on the Defects4J dataset, where our approach generates repairs for 4 bugs that remained unresolved by state-of-the-art baselines. NEVERMORE also fixes another 8 bugs which were only resolved by a subset of these baselines. Finally, we conduct an in-depth analysis of the impact of input and training styles on NEVERMORE's performance, revealing where the chosen style influences the model's bug-fixing capabilities.

The recent advent of data protection laws and regulations has emerged to protect privacy and personal information of individuals. As the cases of privacy breaches and vulnerabilities are rapidly increasing, people are aware and more concerned about their privacy. These bring a significant attention to software development teams to address privacy concerns in developing software applications. As today’s software development adopts an agile, issue-driven approach, issues in an issue tracking system become a centralised pool that gathers new requirements, requests for modification and all the tasks of the software project. Hence, establishing an alignment between those issues and privacy requirements is an important step in developing privacy-aware software systems. This alignment also facilitates privacy compliance checking which may be required as an underlying part of regulations for organisations. However, manually establishing those alignments is labour intensive and time consuming. In this paper, we explore a wide range of machine learning and natural language processing techniques which can automatically classify privacy requirements in issue reports. We employ six popular techniques namely Bag-of-Words (BoW), N-gram Inverse Document Frequency (N-gram IDF), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) to perform the classification on privacy-related issue reports in Google Chrome and Moodle projects. The evaluation showed that BoW, N-gram IDF, TF-IDF and Word2Vec techniques are suitable for classifying privacy requirements in those issue reports. In addition, N-gram IDF is the best performer in both projects.

The Crowdsourced IoT Service (CIS) market is inherently different from other service markets, e.g., web services and cloud. The CIS market is dominated by transient services as both consumers and providers are dynamic in space and time. Consumer requests are usually long-term and demand continuity in service provision. We propose a novel egalitarian transient service composition framework from the CIS market perspective. We apply a Dynamic Bayesian Network to model the dynamic service provision behavior of the providers. The proposed framework transforms the composition of transient services into a multi-objective temporal optimization, i.e., providing continuous services to the maximum number of consumers, and minimizing the consumers' cost of service usages over a long-term period. We incorporate a Pareto-based genetic algorithm to enable the fair distribution of services among the consumers. Experimental results prove the efficiency of the proposed approach in terms of continuous availability of service as well as fair distribution among consumers.