237 research outputs found

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    LASSO – an observatorium for the dynamic selection, analysis and comparison of software

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    Mining software repositories at the scale of 'big code' (i.e., big data) is a challenging activity. As well as finding a suitable software corpus and making it programmatically accessible through an index or database, researchers and practitioners have to establish an efficient analysis infrastructure and precisely define the metrics and data extraction approaches to be applied. Moreover, for analysis results to be generalisable, these tasks have to be applied at a large enough scale to have statistical significance, and if they are to be repeatable, the artefacts need to be carefully maintained and curated over time. Today, however, a lot of this work is still performed by human beings on a case-by-case basis, with the level of effort involved often having a significant negative impact on the generalisability and repeatability of studies, and thus on their overall scientific value. The general purpose, 'code mining' repositories and infrastructures that have emerged in recent years represent a significant step forward because they automate many software mining tasks at an ultra-large scale and allow researchers and practitioners to focus on defining the questions they would like to explore at an abstract level. However, they are currently limited to static analysis and data extraction techniques, and thus cannot support (i.e., help automate) any studies which involve the execution of software systems. This includes experimental validations of techniques and tools that hypothesise about the behaviour (i.e., semantics) of software, or data analysis and extraction techniques that aim to measure dynamic properties of software. In this thesis a platform called LASSO (Large-Scale Software Observatorium) is introduced that overcomes this limitation by automating the collection of dynamic (i.e., execution-based) information about software alongside static information. It features a single, ultra-large scale corpus of executable software systems created by amalgamating existing Open Source software repositories and a dedicated DSL for defining abstract selection and analysis pipelines. Its key innovations are integrated capabilities for searching for selecting software systems based on their exhibited behaviour and an 'arena' that allows their responses to software tests to be compared in a purely data-driven way. We call the platform a 'software observatorium' since it is a place where the behaviour of large numbers of software systems can be observed, analysed and compared

    Exploring Social Hierarchy Computationally to Further Our Understanding of Social Organizations Within Their Environments

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    Hierarchy is ever-present across countless human societies, a seemingly inescapable reality of small organizations and national governments. However, there is a lot about hierarchy we don’t understand, and if we want to make better organizations and better society, it is crucial we learn more about it. This dissertation investigates three questions: 1) “What is hierarchy?” 2) “How is hierarchy useful?” 3) “How does hierarchy vary?” I find that social scientists do not all mean the same thing by hierarchy, even within the same fields; yet, they do consistently write of hierarchy as control (like boss-employee relations), hierarchy as rank (like social class relations), and hierarchy as nested structure (like cities in states), so future scholars can and should be clear in what they mean. Next, I use a computer simulation to show that control hierarchy can be useful in changing environments where workers see local views of change and managers see the big picture—a tension that is unavoidable in such environments. Hierarchy can make this tension useful if and only if the workers have autonomy to weigh the manager’s information about the environment in their decisions; if they must obey the manager no matter what, then they do very poorly in nearly all types of changing environments. Lastly, I use workforce data from US federal agencies to look at organizational structure and control hierarchy in agencies from 2004 to 2021. I find that hierarchy is similar across most agencies, suggesting that overall, hierarchy relates more to scale than function. However, agencies with offices spread across the nation are different from the others, with more and broader management at higher levels. I also find that agencies vary in their organizational structure in other ways, such as the number of distinct occupations they have, and the number of formal rules they must follow, in patterns that are predictable based on their mission statements and agency type; form does follow function. Overall, this dissertation shows that the use of computational techniques in the study of hierarchy can provide great insight, and help us understand organizations more generally

    Contributions to modeling with set-valued data: benefitting from undecided respondents

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    This dissertation develops a methodological framework and approaches to benefit from undecided survey participants, particularly undecided voters in pre-election polls. As choices can be seen as processes that - in stages - exclude alternatives until arriving at one final element, we argue that in pre-election polls undecided participants can most suitably be represented by the set of their viable options. This consideration set sampling, in contrast to the conventional neglection of the undecided, could reduce nonresponse and collects new and valuable information. We embed the resulting set-valued data in the framework of random sets, which allows for two different interpretations, and develop modeling methods for either one. The first interpretation is called ontic and views the set of options as an entity of its own that most accurately represents the position at the time of the poll, thus as a precise representation of something naturally imprecise. With this, new ways of structural analysis emerge as individuals pondering between particular parties can now be examined. We show how the underlying categorical data structure can be preserved in this formalization process for specific models and how popular methods for categorical data analysis can be broadly transferred. As the set contains the eventual choice, under the second interpretation, the set is seen as a coarse version of an underlying truth, which is called the epistemic view. This imprecise information of something actually precise can then be used to improve predictions or election forecasting. We developed several approaches and a framework of a factorized likelihood to utilize the set-valued information for forecasting. Amongst others, we developed methods addressing the complex uncertainty induced by the undecided, weighting the justifiability of assumptions with the conciseness of the results. To evaluate and apply our approaches, we conducted a pre-election poll for the German federal election of 2021 in cooperation with the polling institute Civey, for the first time regarding undecided voters in a set-valued manner. This provides us with the unique opportunity to demonstrate the advantages of the new approaches based on a state-of-the-art survey

    JavaScript SBST Heuristics to Enable Effective Fuzzing of NodeJS Web APIs

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    JavaScript is one of the most popular programming languages. However, its dynamic nature poses several challenges to automated testing techniques. In this paper, we propose an approach and open-source tool support to enable white-box testing of JavaScript applications using Search-Based Software Testing (SBST) techniques. We provide an automated approach to collect search-based heuristics like the common Branch Distance and to enable Testability Transformations. To empirically evaluate our results, we integrated our technique into the EvoMaster test generation tool, and carried out analyses on the automated system testing of RESTful and GraphQL APIs. Experiments on eight Web APIs running on NodeJS show that our technique leads to significantly better results than existing black-box and grey-box testing tools, in terms of code coverage and fault detection.publishedVersio

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    An Algorithmic Theory of the Policy Process

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    With a few exceptions, current theories of the policy process do not model or measure the policy process using the graphical process notations that are common within information science, business administration and many natural sciences. The reason is that in the post-war period the needs of business process analysis came to dominate social science applications of process science whilst the needs of public policy process analysis remained largely unaddressed. As a result, modern graphical process notations can encode and quantify the instrumental properties of cost and efficiency of a business process, but not the normative properties of transparency, accountability or legitimacy of the much more complex policy making process. There have been many other unfortunate consequences. Business process modelling evolved into business process reengineering and became a critical enabler of a period of unprecedented hyper-globalization commencing in the 1990’s. However, it did so by encoding and quantifying the instrumental dimensions of cost and efficiency of globalized production processes and not their normative dimensions of domestic employment and social welfare transfers. We live with the consequences to this day of the emergence of destabilizing populist national movements and rising security and defense tensions between former trading partners. However, in recent years, there have been several important new developments. Firstly, a new class of process modelling tools has emerged at the juncture of the disciplines of information science and business administration that can model much more complex governance and policy-making processes as rules based declarative process graphs instead of sequence based imperative process graphs. Secondly, information science is now introducing a capacity for normative reasoning and moral dilemma resolution into a range of technologies from multi-agent systems and artificial societies to self-driving vehicles and autonomous battle drones. This creates new opportunities for a collaboration between policy process analysis and information science to reengineer legacy policy making processes and organizations in terms of normatively driven declarative processes. Not only must these reengineered policy making processes score better against instrumental criteria of cost and efficiency but also against the normative criteria of transparency, accountability, and legitimacy. Consequently, the metrics presented in this dissertation re-connect public policy process analysis with the tools and results of decades of process research in the fields of information science, business administration and many natural sciences, and supports a new theory of the public policy process as an algorithm whose purpose is the generation of solutions to public goods allocation problems. To illustrate the principles of the techniques involved and the utility of the approach, a case study analysis and prediction of Chinese public health policy response to the COVID-19 pandemic of 2020/21 is presented

    Certifications of Critical Systems – The CECRIS Experience

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    In recent years, a considerable amount of effort has been devoted, both in industry and academia, to the development, validation and verification of critical systems, i.e. those systems whose malfunctions or failures reach a critical level both in terms of risks to human life as well as having a large economic impact.Certifications of Critical Systems – The CECRIS Experience documents the main insights on Cost Effective Verification and Validation processes that were gained during work in the European Research Project CECRIS (acronym for Certification of Critical Systems). The objective of the research was to tackle the challenges of certification by focusing on those aspects that turn out to be more difficult/important for current and future critical systems industry: the effective use of methodologies, processes and tools.The CECRIS project took a step forward in the growing field of development, verification and validation and certification of critical systems. It focused on the more difficult/important aspects of critical system development, verification and validation and certification process. Starting from both the scientific and industrial state of the art methodologies for system development and the impact of their usage on the verification and validation and certification of critical systems, the project aimed at developing strategies and techniques supported by automatic or semi-automatic tools and methods for these activities, setting guidelines to support engineers during the planning of the verification and validation phases
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