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Large Language Models (LLMs) have shown strong performance in solving mathematical problems, with code-based solutions proving particularly effective. However, the best practice to leverage coding instruction data to enhance mathematical reasoning remains underexplored. This study investigates three key questions: (1) How do different coding styles of mathematical code-based rationales impact LLMs' learning performance? (2) Can general-domain coding instructions improve performance? (3) How does integrating textual rationales with code-based ones during training enhance mathematical reasoning abilities? Our findings reveal that code-based rationales with concise comments, descriptive naming, and hardcoded solutions are beneficial, while improvements from general-domain coding instructions and textual rationales are relatively minor. Based on these insights, we propose CoinMath, a learning strategy designed to enhance mathematical reasoning by diversifying the coding styles of code-based rationales. CoinMath generates a variety of code-based rationales incorporating concise comments, descriptive naming conventions, and hardcoded solutions. Experimental results demonstrate that CoinMath significantly outperforms its baseline model, MAmmoTH, one of the SOTA math LLMs.

The frequent discovery of security vulnerabilities in both open-source and proprietary software underscores the urgent need for earlier detection during the development lifecycle. Initiatives such as DARPA's Artificial Intelligence Cyber Challenge (AIxCC) aim to accelerate Automated Vulnerability Detection (AVD), seeking to address this challenge by autonomously analyzing source code to identify vulnerabilities. This paper addresses two primary research questions: (RQ1) How is current AVD research distributed across its core components? (RQ2) What key areas should future research target to bridge the gap in the practical applicability of AVD throughout software development? To answer these questions, we conduct a systematization over 79 AVD articles and 17 empirical studies, analyzing them across five core components: task formulation and granularity, input programming languages and representations, detection approaches and key solutions, evaluation metrics and datasets, and reported performance. Our systematization reveals that the narrow focus of AVD research-mainly on specific tasks and programming languages-limits its practical impact and overlooks broader areas crucial for effective, real-world vulnerability detection. We identify significant challenges, including the need for diversified problem formulations, varied detection granularities, broader language support, better dataset quality, enhanced reproducibility, and increased practical impact. Based on these findings we identify research directions that will enhance the effectiveness and applicability of AVD solutions in software security.

Middleware, third-party software intermediaries between users and platforms, has been broached as a means to decentralize the power of social media platforms and enhance user agency. Middleware may enable a more user-centric and democratic approach to shaping digital experiences, offering a flexible architecture as an alternative to both centrally controlled, opaque platforms and an unmoderated, uncurated internet. The widespread adoption of open middleware has long hinged on the cooperation of established major platforms; however, the recent growth of federated platforms, such as Mastodon and Bluesky, has led to increased offerings and user awareness. In this report we consider the potential of middleware as a means of enabling greater user control over curation and moderation - two aspects of the social media experience that are often mired in controversy. We evaluate the trade-offs and negative externalities it might create, and discuss the technological, regulatory, and market dynamics that could either support or hinder its implementation.

Software development support tools have been studied for a long time, with recent approaches using Large Language Models (LLMs) for code generation. These models can generate Python code for data science and machine learning applications. LLMs are helpful for software engineers because they increase productivity in daily work. An LLM can also serve as a "mentor" for inexperienced software developers, and be a viable learning support. High-quality code generation with LLMs can also be beneficial in geospatial data science. However, this domain poses different challenges, and code generation LLMs are typically not evaluated on geospatial tasks. Here, we show how we constructed an evaluation benchmark for code generation models, based on a selection of geospatial tasks. We categorised geospatial tasks based on their complexity and required tools. Then, we created a dataset with tasks that test model capabilities in spatial reasoning, spatial data processing, and geospatial tools usage. The dataset consists of specific coding problems that were manually created for high quality. For every problem, we proposed a set of test scenarios that make it possible to automatically check the generated code for correctness. In addition, we tested a selection of existing code generation LLMs for code generation in the geospatial domain. We share our dataset and reproducible evaluation code on a public GitHub repository, arguing that this can serve as an evaluation benchmark for new LLMs in the future. Our dataset will hopefully contribute to the development new models capable of solving geospatial coding tasks with high accuracy. These models will enable the creation of coding assistants tailored for geospatial applications.

As one of the most popular software applications, a web application is a program, accessible through the web, to dynamically generate content based on user interactions or contextual data, for example, online shopping platforms, social networking sites, and financial services. Web applications operate in diverse environments and leverage web technologies such as HTML, CSS, JavaScript, and Ajax, often incorporating features like asynchronous operations to enhance user experience. Due to the increasing user and popularity of web applications, approaches to their quality have become increasingly important. Web Application Testing (WAT) plays a vital role in ensuring web applications' functionality, security, and reliability. Given the speed with which web technologies are evolving, WAT is especially important. Over the last decade, various WAT approaches have been developed. The diversity of approaches reflects the many aspects of web applications, such as dynamic content, asynchronous operations, and diverse user environments. This paper provides a comprehensive overview of the main achievements during the past decade: It examines the main steps involved in WAT, including test-case generation and execution, and evaluation and assessment. The currently available tools for WAT are also examined. The paper also discusses some open research challenges and potential future WAT work.

Code documentation is a critical aspect of software development, serving as a bridge between human understanding and machine-readable code. Beyond assisting developers in understanding and maintaining code, documentation also plays a critical role in automating various software engineering tasks, such as test oracle generation (TOG). In Java, Javadoc comments provide structured, natural language documentation embedded directly in the source code, typically detailing functionality, usage, parameters, return values, and exceptions. While prior research has utilized Javadoc comments in test oracle generation (TOG), there has not been a thorough investigation into their impact when combined with other contextual information, nor into identifying the most relevant components for generating correct and strong test oracles, or understanding their role in detecting real bugs. In this study, we dive deep into investigating the impact of Javadoc comments on TOG.

The widespread adoption of cloud-based solutions introduces privacy and security concerns. Techniques such as homomorphic encryption (HE) mitigate this problem by allowing computation over encrypted data without the need for decryption. However, the high computational and memory overhead associated with the underlying cryptographic operations has hindered the practicality of HE-based solutions. While a significant amount of research has focused on reducing computational overhead by utilizing hardware accelerators like GPUs and FPGAs, there has been relatively little emphasis on addressing HE memory overhead. Processing in-memory (PIM) presents a promising solution to this problem by bringing computation closer to data, thereby reducing the overhead resulting from processor-memory data movements. In this work, we evaluate the potential of a PIM architecture from UPMEM for accelerating HE operations. Firstly, we focus on PIM-based acceleration for polynomial operations, which underpin HE algorithms. Subsequently, we conduct a case study analysis by integrating PIM into two popular and open-source HE libraries, OpenFHE and HElib. Our study concludes with key findings and takeaways gained from the practical application of HE operations using PIM, providing valuable insights for those interested in adopting this technology.

Modern artificial intelligence (AI) applications require large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality. For example, incomplete, erroneous, or inappropriate training data can lead to unreliable models that ultimately produce poor decisions. Trustworthy AI applications require high-quality training and test data along many quality dimensions, such as accuracy, completeness, and consistency. We explore empirically the relationship between six data quality dimensions and the performance of 19 popular machine learning algorithms covering the tasks of classification, regression, and clustering, with the goal of explaining their performance in terms of data quality. Our experiments distinguish three scenarios based on the AI pipeline steps that were fed with polluted data: polluted training data, test data, or both. We conclude the paper with an extensive discussion of our observations.

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose $200+$ concrete research questions.

The fusion of causal models with deep learning introducing increasingly intricate data sets, such as the causal associations within images or between textual components, has surfaced as a focal research area. Nonetheless, the broadening of original causal concepts and theories to such complex, non-statistical data has been met with serious challenges. In response, our study proposes redefinitions of causal data into three distinct categories from the standpoint of causal structure and representation: definite data, semi-definite data, and indefinite data. Definite data chiefly pertains to statistical data used in conventional causal scenarios, while semi-definite data refers to a spectrum of data formats germane to deep learning, including time-series, images, text, and others. Indefinite data is an emergent research sphere inferred from the progression of data forms by us. To comprehensively present these three data paradigms, we elaborate on their formal definitions, differences manifested in datasets, resolution pathways, and development of research. We summarize key tasks and achievements pertaining to definite and semi-definite data from myriad research undertakings, present a roadmap for indefinite data, beginning with its current research conundrums. Lastly, we classify and scrutinize the key datasets presently utilized within these three paradigms.