Static Code Analysis in the AI Era: An In-depth Exploration of the Concept, Function, and Potential of Intelligent Code Analysis Agents

The escalating complexity of software systems and accelerating development cycles pose a significant challenge in managing code errors and implementing business logic. Traditional techniques, while cornerstone for software quality assurance, exhibit limitations in handling intricate business logic and extensive codebases. To address these challenges, we introduce the Intelligent Code Analysis Agent (ICAA), a novel concept combining AI models, engineering process designs, and traditional non-AI components. The ICAA employs the capabilities of large language models (LLMs) such as GPT-3 or GPT-4 to automatically detect and diagnose code errors and business logic inconsistencies. In our exploration of this concept, we observed a substantial improvement in bug detection accuracy, reducing the false-positive rate to 66\% from the baseline's 85\%, and a promising recall rate of 60.8\%. However, the token consumption cost associated with LLMs, particularly the average cost for analyzing each line of code, remains a significant consideration for widespread adoption. Despite this challenge, our findings suggest that the ICAA holds considerable potential to revolutionize software quality assurance, significantly enhancing the efficiency and accuracy of bug detection in the software development process. We hope this pioneering work will inspire further research and innovation in this field, focusing on refining the ICAA concept and exploring ways to mitigate the associated costs

Solving package dependencies: from EDOS to Mancoosi

Mancoosi (Managing the Complexity of the Open Source Infrastructure) is an ongoing research project funded by the European Union for addressing some of the challenges related to the "upgrade problem" of interdependent software components of which Debian packages are prototypical examples. Mancoosi is the natural continuation of the EDOS project which has already contributed tools for distribution-wide quality assurance in Debian and other GNU/Linux distributions. The consortium behind the project consists of several European public and private research institutions as well as some commercial GNU/Linux distributions from Europe and South America. Debian is represented by a small group of Debian Developers who are working in the ranks of the involved universities to drive and integrate back achievements into Debian. This paper presents relevant results from EDOS in dependency management and gives an overview of the Mancoosi project and its objectives, with a particular focus on the prospective benefits for Debian

Integration of Quality Attributes in Software Product Line Development

Different approaches for building modern software systems in complex and open environments have been proposed in the last few years. Some efforts try to apply Software Product Line (SPL) approach to take advantage of the massive reuse for producing software systems that share a common set of features. In general quality assurance is a crucial activity for success in software industry, but it is even more important when talking about Software Product Lines since the intensive reuse of assets makes the quality attributes (a measurable physical or abstract property of an entity) of the assets to be transmitted to the whole SPL scope. However, despite the importance that quality has in software product line development, most of the methodologies being applied in Software Product Line Development focus only on managing the commonalities and variability within the product line and not giving support to the non--¿ functional requirements that the products must fit. The main goal of this master final work is to introduce quality attributes in early stages of software product line development processes by means of the definition of a production plan that, on one hand, integrates quality as an additional view for describing the extension of the software product line and, on the other hand introduces the quality attributes as a decision factor during product configuration and when selecting among design alternatives. Our approach has been defined following the Model--¿ Driven Software Development paradigm. Therefore all the software artifacts defined had its correspondent metamodels and the processes defined rely on automated model transformations. Finally in order to illustrate the feasibility of the approach we have integrated the quality view in an SPL example in the context of safety critical embedded systems on the automotive domain.González Huerta, J. (2011). Integration of Quality Attributes in Software Product Line Development. http://hdl.handle.net/10251/15835Archivo delegad

Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented Approach

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty, and that it is effective and efficient in providing assurances for the goals of self-adaptive systems

KAPTUR: technical analysis report

Led by the Visual Arts Data Service (VADS) and funded by the JISC Managing Research Data programme (2011-13) KAPTUR will discover, create and pilot a sectoral model of best practice in the management of research data in the visual arts in collaboration with four institutional partners: Glasgow School of Art; Goldsmiths, University of London; University for the Creative Arts; and University of the Arts London. This report is framed around the research question: which technical system is most suitable for managing visual arts research data? The first stage involved a literature review including information gathered through attendance at meetings and events, and Internet research, as well as information on projects from the previous round of JISCMRD funding (2009-11). During February and March 2012, the Technical Manager carried out interviews with the four KAPTUR Project Officers and also met with IT staff at each institution. This led to the creation of a user requirement document (Appendix A), which was then circulated to the project team for additional comments and feedback. The Technical Manager selected 17 systems to compare with the user requirement document (Appendix B). Five of the systems had similar scores so these were short-listed. The Technical Manager created an online form into which the Project Officers entered priority scores for each of the user requirements in order to calculate a more accurate score for each of the five short-listed systems (Appendix C) and this resulted in the choice of EPrints as the software for the KAPTUR project