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Software engineers integrate third-party components into their applications. The resulting software supply chain is vulnerable. To reduce the attack surface, we can verify the origin of components (provenance) before adding them. Cryptographic signatures enable this. This article describes traditional signing, its challenges, and changes introduced by next generation signing platforms

This paper systematizes knowledge about secure software supply chain patterns. It identifies four stages of a software supply chain attack and proposes three security properties crucial for a secured supply chain: transparency, validity, and separation. The paper describes current security approaches and maps them to the proposed security properties, including research ideas and case studies of supply chains in practice. It discusses the strengths and weaknesses of current approaches relative to known attacks and details the various security frameworks put out to ensure the security of the software supply chain. Finally, the paper highlights potential gaps in actor and operation-centered supply chain security techniques, Comment: Published in the proceedings of the 1st ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses (SCORED) 2022

Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including computer vision, system configuration, and question-answering. However, DNNs are expensive to develop, both in intellectual effort (e.g., devising new architectures) and computational costs (e.g., training). Reusing DNNs is a promising direction to amortize costs within a company and across the computing industry. As with any new technology, however, there are many challenges in reusing DNNs. These challenges include both missing technical capabilities and missing engineering practices. This vision paper describes challenges in current approaches to DNN re-use. We summarize studies of re-use failures across the spectrum of re-use techniques, including conceptual (e.g., reusing based on a research paper), adaptation (e.g., re-using by building on an existing implementation), and deployment (e.g., direct re-use on a new device). We outline possible advances that would improve each kind of re-use., Comment: Proceedings of the IEEE John Vincent Atanasoff Symposium on Modern Computing (JVA'23) 2023

This paper conducts a user study to assess whether three machine learning (ML) interpretability layouts can influence participants' views when evaluating sentences containing hate speech, focusing on the "Misogyny" and "Racism" classes. Given the existence of divergent conclusions in the literature, we provide empirical evidence on using ML interpretability in online communities through statistical and qualitative analyses of questionnaire responses. The Generalized Additive Model estimates participants' ratings, incorporating within-subject and between-subject designs. While our statistical analysis indicates that none of the interpretability layouts significantly influences participants' views, our qualitative analysis demonstrates the advantages of ML interpretability: 1) triggering participants to provide corrective feedback in case of discrepancies between their views and the model, and 2) providing insights to evaluate a model's behavior beyond traditional performance metrics.

Many software applications incorporate open-source third-party packages distributed by public package registries. Guaranteeing authorship along this supply chain is a challenge. Package maintainers can guarantee package authorship through software signing. However, it is unclear how common this practice is, and whether the resulting signatures are created properly. Prior work has provided raw data on registry signing practices, but only measured single platforms, did not consider quality, did not consider time, and did not assess factors that may influence signing. We do not have up-to-date measurements of signing practices nor do we know the quality of existing signatures. Furthermore, we lack a comprehensive understanding of factors that influence signing adoption. This study addresses this gap. We provide measurements across three kinds of package registries: traditional software (Maven, PyPI), container images (DockerHub), and machine learning models (Hugging Face). For each registry, we describe the nature of the signed artifacts as well as the current quantity and quality of signatures. Then, we examine longitudinal trends in signing practices. Finally, we use a quasi-experiment to estimate the effect that various factors had on software signing practices. To summarize our findings: (1) mandating signature adoption improves the quantity of signatures; (2) providing dedicated tooling improves the quality of signing; (3) getting started is the hard part -- once a maintainer begins to sign, they tend to continue doing so; and (4) although many supply chain attacks are mitigable via signing, signing adoption is primarily affected by registry policy rather than by public knowledge of attacks, new engineering standards, etc. These findings highlight the importance of software package registry managers and signing infrastructure., Comment: Accepted at IEEE Security & Privacy 2024 (S&P'24)

The primary theory of software engineering is that an organization's Policies and Processes influence the quality of its Products. We call this the PPP Theory. Although empirical software engineering research has grown common, it is unclear whether researchers are trying to evaluate the PPP Theory. To assess this, we analyzed half (33) of the empirical works published over the last two years in three prominent software engineering conferences. In this sample, 70% focus on policies/processes or products, not both. Only 33% provided measurements relating policy/process and products. We make four recommendations: (1) Use PPP Theory in study design; (2) Study feedback relationships; (3) Diversify the studied feedforward relationships; and (4) Disentangle policy and process. Let us remember that research results are in the context of, and with respect to, the relationship between software products, processes, and policies., Comment: 5 pages, published in the proceedings of the 2023 ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering in the Ideas-Visions-Reflections track (ESEC/FSE-IVR'23)

As we increasingly depend on software systems, the consequences of breaches in the software supply chain become more severe. High-profile cyber attacks like those on SolarWinds and ShadowHammer have resulted in significant financial and data losses, underlining the need for stronger cybersecurity. One way to prevent future breaches is by studying past failures. However, traditional methods of analyzing these failures require manually reading and summarizing reports about them. Automated support could reduce costs and allow analysis of more failures. Natural Language Processing (NLP) techniques such as Large Language Models (LLMs) could be leveraged to assist the analysis of failures. In this study, we assessed the ability of Large Language Models (LLMs) to analyze historical software supply chain breaches. We used LLMs to replicate the manual analysis of 69 software supply chain security failures performed by members of the Cloud Native Computing Foundation (CNCF). We developed prompts for LLMs to categorize these by four dimensions: type of compromise, intent, nature, and impact. GPT 3.5s categorizations had an average accuracy of 68% and Bard had an accuracy of 58% over these dimensions. We report that LLMs effectively characterize software supply chain failures when the source articles are detailed enough for consensus among manual analysts, but cannot yet replace human analysts. Future work can improve LLM performance in this context, and study a broader range of articles and failures., Comment: 22 pages, 9 figures

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as "model hubs" support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult - there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data. We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset. The PTMTorrent dataset (v1) is available at: https://app.globus.org/file-manager?origin_id=55e17a6e-9d8f-11ed-a2a2-8383522b48d9&origin_path=%2F~%2F. Our dataset generation tools are available on GitHub: https://doi.org/10.5281/zenodo.7570357., Comment: 5 pages, 2 figures, Accepted to MSR'23

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems. In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. Based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. Three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries., Comment: Proceedings of the ACM/IEEE 45th International Conference on Software Engineering (ICSE) 2023

When people need help with their day-to-day activities, they turn to family, friends or neighbours. But despite an increasingly networked world, technology falls short in finding suitable volunteers. In this paper, we propose uHelp, a platform for building a community of helpful people and supporting community members find the appropriate help within their social network. Lately, applications that focus on finding volunteers have started to appear, such as Helpin or Facebook's Community Help. However, what distinguishes uHelp from existing applications is its trust-based intelligent search for volunteers. Although trust is crucial to these innovative social applications, none of them have seriously achieved yet a trust-building solution such as that of uHelp. uHelp's intelligent search for volunteers is based on a number of AI technologies: (1) a novel trust-based flooding algorithm that navigates one's social network looking for appropriate trustworthy volunteers; (2) a novel trust model that maintains the trustworthiness of peers by learning from their similar past experiences; and (3) a semantic similarity model that assesses the similarity of experiences. This article presents the uHelp application, describes the underlying AI technologies that allow uHelp find trustworthy volunteers efficiently, and illustrates the implementation details. uHelp's initial prototype has been tested with a community of single parents in Barcelona, and the app is available online at both Apple Store and Google Play.

In the sub-TeV regime, the most widely used hadronic interaction models disagree significantly in their predictions of particle spectra from cosmic ray induced air showers. We investigate the nature and impact of model uncertainties, focussing on air shower primaries with energies around the transition between high and low energy hadronic interaction models, where the dissimilarities are largest and which constitute the bulk of the interactions in air showers., Comment: Proceedings of the 51 International Symposium on Multiparticle Dynamics (ISMD2022)

In this paper, we focus on normative systems for online communities. The paper addresses the issue that arises when different community members interpret these norms in different ways, possibly leading to unexpected behavior in interactions, usually with norm violations that affect the individual and community experiences. To address this issue, we propose a framework capable of detecting norm violations and providing the violator with information about the features of their action that makes this action violate a norm. We build our framework using Machine Learning, with Logistic Model Trees as the classification algorithm. Since norm violations can be highly contextual, we train our model using data from the Wikipedia online community, namely data on Wikipedia edits. Our work is then evaluated with the Wikipedia use case where we focus on the norm that prohibits vandalism in Wikipedia edits., Comment: proceedings for International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE), co-located with AAMAS 2021

During the shutdown of the CERN Large Hadron Collider in 2013-2014, an additional pixel layer was installed between the existing Pixel detector of the ATLAS experiment and a new, smaller radius beam pipe. The motivation for this new pixel layer, the Insertable B-Layer (IBL), was to maintain or improve the robustness and performance of the ATLAS tracking system, given the higher instantaneous and integrated luminosities realised following the shutdown. Because of the extreme radiation and collision rate environment, several new radiation-tolerant sensor and electronic technologies were utilised for this layer. This paper reports on the IBL construction and integration prior to its operation in the ATLAS detector., Comment: 90 pages in total. Author list: ATLAS IBL Collaboration, starting page 2. 69 figures, 20 tables. Published in Journal of Instrumentation. All figures available at: https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PLOTS/PIX-2018-001

The problem of achieving common understanding between agents that use different vocabularies has been mainly addressed by designing techniques that explicitly negotiate mappings between their vocabularies, requiring agents to share a meta-language. In this paper we consider the case of agents that use different vocabularies and have no meta-language in common, but share the knowledge of how to perform a task, given by the specification of an interaction protocol. For this situation, we present a framework that lets agents learn a vocabulary alignment from the experience of interacting. Unlike previous work in this direction, we use open protocols that constrain possible actions instead of defining procedures, making our approach more general. We present two techniques that can be used either to learn an alignment from scratch or to repair an existent one, and we evaluate experimentally their performance., Comment: 14 pages, accepted at AAMAS 2017