An Extensible Framework for Creating Personal Archives of Web Resources Requiring Authentication

The key factors for the success of the World Wide Web are its large size and the lack of a centralized control over its contents. In recent years, many advances have been made in preserving web content but much of this content (namely, social media content) was not archived, or still to this day is not being archived,for various reasons. Tools built to accomplish this frequently break because of the dynamic structure of social media websites. Because many social media websites exhibit a commonality in hierarchy of the content, it would be worthwhile to setup a means to reference this hierarchy for tools to leverage and become adaptive as the target websites evolve. As relying on the service to provide this means is problematic in the context of archiving, we can surmise that the only way to assure that all of these shortcomings are not experienced is to rely on the original context in which the user views the content, i.e. the webbrowser. In this thesis I will describe an abstract specification and concrete implementations of the specification that allow tools to leverage the context of theweb browser to capture content into personal web archives. These tools will then be able to accomplish personal web archiving in a way that makes them more robust. As evaluation, I will make a change in the hierarchy of a synthetic social media website and its respective specification. Then, I will show that anadapted tool, using the specification, continues to function and is able to archive the social media website

SOFTENG 2023: the ninth international conference on advances and trends in software engineering

The Ninth International Conference on Advances and Trends in Software Engineering (SOFTENG 2023), held between April 24th and April 28th, 2023, continued a series of events focusing on these challenging aspects for software development and deployment, across the whole life-cycle. Software engineering exhibits challenging dimensions in the light of new applications, devices, and services. Mobility, user-centric development, smart-devices, e-services, ambient environments, e-health and wearable/implantable devices pose specific challenges for specifying software requirements and developing reliable and safe software. Specific software interfaces, agile organization and software dependability require particular approaches for software security, maintainability, and sustainability. We take here the opportunity to warmly thank all the members of the SOFTENG 2023 technical program committee, as well as all the reviewers. The creation of such a high-quality conference program would not have been possible without their involvement. We also kindly thank all the authors who dedicated much of their time and effort to contribute to SOFTENG 2023. We truly believe that, thanks to all these efforts, the final conference program consisted of top-quality contributions. We also thank the members of the SOFTENG 2023 organizing committee for their help in handling the logistics of this event. We hope that SOFTENG 2023 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in the field of software engineering

Temporal Models For History-Aware Explainability In Self-Adaptive Systems

The complexity of real-world problems requires modern software systems to be able to autonomously adapt and modify their behaviour at runtime to deal with unforeseen internal and external fluctuations and contexts. Consequently, these self-adaptive systems (SAS) can show unexpected and surprising behaviours which stakeholders may not understand or agree with. This may be exacerbated due to the ubiquity and complexity of Artificial Intelligence (AI) techniques which are often considered “black boxes” and are increasingly used by SAS. This thesis explores how synergies between model-driven engineering and runtime monitoring help to enable explanations based on SAS’ historical behaviour with the objective of promoting transparency and understandability in these types of systems. Specifically, this PhD work has studied how the use of runtime models extended with long-term memory can provide the abstraction, analysis and reasoning capabilities needed to support explanations when using AI-based SAS. For this purpose, this work argues that a system should i) offer access and retrieval of historical data about past behaviour, ii) track over time the reasons for its decision making, and iii) be able to convey this knowledge to different stakeholders as part of explanations for justifying its behaviour. Runtime models stored in Temporal Graph Databases, which result in Temporal Models (TMs), are proposed for tracking the decision-making history of SAS to support explanations. The approach enables explainability for interactive diagnosis (i.e. during execution) and forensic analysis (i.e. after the fact) based on the trajectory of the SAS execution. Furthermore, in cases where the resources are limited (e.g., storage capacity or time to response), the proposed architecture also integrates the runtime monitoring technique, complex event processing (CEP). CEP allows detecting matches to event patterns that need to be stored instead of keeping the entire history. The proposed architecture helps developers in gaining insights into SAS while they work on validating and improving their systems