arXiv subjects

João R. Campos

Publications and source records attributed to João R. Campos.

8 recordsLinked to original sources

arXiv2026

Understanding Online Failure Prediction in Linux Through Complementary Multi-View Explainability

Accurate Online Failure Prediction (OFP) has been shown to be feasible in Operating Systems (OSs) settings, but prediction alone is not sufficient for practical adoption. Without diagnostic insight, operators have limited basis to trust alerts or decide how to respond. Moreover, even when predictive accuracy is high, it is often unclear whether models are capturing meaningful failure processes or merely exploiting workload-specific noise and incidental correlations in telemetry. This paper reports a practical experience building and evaluating an explainable OFP pipeline for Linux OSs. We combine consensus-based feature selection for detection with temporal onset analysis, subsystemlevel causal analysis, and complementary diagnostic mechanisms to support failure interpretation. Evaluated under strict crossworkload conditions with frozen training artifacts, it achieved 91-94% detection on unseen workloads without retraining, while maintaining false alarm rates below 1%. However, failure mode diagnosis proved substantially more sensitive to workload shift, and several diagnostics mechanisms showed limited effectiveness for specific failure types. Our experience highlights three main lessons: i) detection generalizes more robustly than diagnosis across workload changes; ii) early-warning capability depends strongly on the failure mode, ranging from 38 to 215 seconds in our study; and iii) unseen failure modes are not reliably diagnosable from related training modes alone, providing 0% accuracy under Leave-One-Mode-Out (LOMO) evaluation. Taken together, these results show the value of complementary explainability mechanisms for interpreting accurate failure predictions, revealing when predictive signals reflect transferable failure structure and when diagnostic generalization breaks down under workload variation.

cs.SE↗

arXiv2026

Unreliable in Practice? A Comprehensive Study of Errors in LLM-Generated Code

Large Language Models (LLMs) are being widely used for coding, with reports indicating that AI now generates an increasing share of production code. Studies show that LLMs can significantly improve developer productivity, yet they still struggle with more complex coding tasks. Just as understanding error modes in human-written code has been central to improving software quality, identifying and characterizing the errors in LLM-generated code is critical for setting realistic expectations and designing mitigation strategies. Prior research has been limited in scope, often focusing on a single language, a small number of problems, or a limited selection of models. As a result, there is still no comprehensive understanding of which errors are common and which are specific to certain models or languages. To address these gaps and develop a deeper understanding of the quality of LLM-generated code, we analyzed a corpus of 86,726 code samples that contained compilation or runtime errors. These samples were generated by seven LLMs across four compiled languages. We classified errors by their underlying causes using an LLM, manually validated these classifications, and performed a comparative analysis. This labeled data is then used to measure error prevalence by model, language, and problem difficulty, to identify common error patterns. Results show that, although error types vary strongly across languages and models, even the largest models frequently make simple mistakes. We also observe that generated code often omits basic input validation or memory-safety checks, which can lead to overflows, resource exhaustion, or other reliability/security issues.

cs.SE↗

arXiv2026

PROBE: Benchmarking Code Generation in Large Language Models

Large Language Models (LLMs) are increasingly being used in everyday software engineering tasks, particularly in automated code generation. Despite their widespread adoption, these models remain far from perfect, making systematic and fair evaluation essential to understand their strengths and limitations. In the context of code generation, existing benchmarks are limited: they often target a single programming language and rely primarily on unit test outcomes, while overlooking other critical dimensions such as the overall quality of the generated code and its closeness to a valid solution. To address these gaps, we introduce PROBE, an extensible benchmark framework that, unlike prior work, establishes a systematic structure built on diverse and well-defined metrics, representative workloads, varied prompt templates, and a robust experimental procedure. In practice, the code generated by the LLMs is evaluated along three complementary dimensions: functional correctness, proximity to valid solutions, and code quality, enabling a comprehensive assessment of performance. We use PROBE to evaluate four open-source and two proprietary models under three prompting strategies across five programming languages. We further complement this analysis with a study of common errors in the code and provide concrete examples, offering clearer insight into where LLMs tend to struggle. Our findings show that, while LLMs achieve promising results, they struggle with harder problems and, in the case of smaller models, with programming languages that have fewer available resources for training, and they often fail due to fundamental and easily avoidable errors that underscore the unreliability of automatically generated code.

cs.SE↗

arXiv2026

Leveraging Large Language Models for Trustworthiness Assessment of Web Applications

The widespread adoption of web applications has made their security a critical concern and has increased the need for systematic ways to assess whether they can be considered trustworthy. However, "trust" assessment remains an open problem as existing techniques primarily focus on detecting known vulnerabilities or depend on manual evaluation, which limits their scalability; therefore, evaluating adherence to secure coding practices offers a complementary, pragmatic perspective by focusing on observable development behaviors. In practice, the identification and verification of secure coding practices are predominantly performed manually, relying on expert knowledge and code reviews, which is time-consuming, subjective, and difficult to scale. This study presents an empirical methodology to automate the trustworthiness assessment of web applications by leveraging Large Language Models (LLMs) to verify adherence to secure coding practices. We conduct a comparative analysis of prompt engineering techniques across five state-of-the-art LLMs, ranging from baseline zero-shot classification to prompts enriched with semantic definitions, structural context derived from call graphs, and explicit instructional guidance. Furthermore, we propose an extension of a hierarchical Quality Model (QM) based on the Logic Score of Preference (LSP), in which LLM outputs are used to populate the model's quality attributes and compute a holistic trustworthiness score. Experimental results indicate that excessive structural context can introduce noise, whereas rule-based instructional prompting improves assessment reliability. The resulting trustworthiness score allows discriminating between secure and vulnerable implementations, supporting the feasibility of using LLMs for scalable and context-aware trust assessment.

cs.CR↗

arXiv2026

Propheticus: Machine Learning Framework for the Development of Predictive Models for Reliable and Secure Software

The growing complexity of software calls for innovative solutions that support the deployment of reliable and secure software. Machine Learning (ML) has shown its applicability to various complex problems and is frequently used in the dependability domain, both for supporting systems design and verification activities. However, using ML is complex and highly dependent on the problem in hand, increasing the probability of mistakes that compromise the results. In this paper, we introduce Propheticus, a ML framework that can be used to create predictive models for reliable and secure software systems. Propheticus attempts to abstract the complexity of ML whilst being easy to use and accommodating the needs of the users. To demonstrate its use, we present two case studies (vulnerability prediction and online failure prediction) that show how it can considerably ease and expedite a thorough ML workflow.

cs.LG↗

arXiv2026

Effectiveness of LLM-based Software Diversity for Reliability Improvement -- an Empirical Study

Software diversity has been extensively studied as a means of reducing the risk of common-mode failures. Classic work showed that the central issue is whether failures of diversely redundant components overlap in ways that limit the reliability gains. Traditional software diversity is costly to obtain, since it requires multiple implementations as well as the corresponding validation, maintenance, and deployment effort. Recent advances in Large Language Models (LLMs) may change this. LLMs enable inexpensive code generation: they produce many candidate implementations of the same specification quickly, across different models, decoding settings, and programming languages. This raises a natural question: can LLMs serve as practical generators of software diversity, and how much reliability improvement can that diversity actually provide? In this paper, we extend classical empirical studies of software diversity in human-written programs to LLM-generated code. We study three specifications using both historical human-written programs and large pools of LLM-generated ones evaluated under a common compilation, sandboxing, and exhaustive test suite. We explore LLM diversity along multiple axes, including model family, generation temperature, and programming language. Reliability improvement is evaluated in a 1-out-of-2 configuration across both homogeneous and heterogeneous program populations, including within-LLM pairings and pairings across programming languages and across LLM-generated and human-written programs. The results show that combining LLM-generated programs, especially in heterogeneous settings, can yield reliability gains, although this is partly conditioned by the programming language and generation setting. Taken together, these findings suggest that LLMs provide a scalable source of comparatively low-cost programs whose diversity can be leveraged for reliability improvement.

cs.SE↗

arXiv2026

A Systematic Methodology for Evaluating Failure Independence in LLM-Generated Code

N-Version Programming (NVP) improves software reliability by executing multiple independent implementations and combining outputs, but its adoption is limited by high cost and the assumption of failure independence, which empirical studies have challenged. Recent advances in Large Language Models (LLMs) reduce the cost of generating multiple implementations, shifting focus to whether their failures are independent. We propose the first systematic methodology to assess failure independence in LLM-generated code and apply it to 224 problems across twelve models, five languages, and three prompting strategies. We analyze both structural and behavioral diversity (i.e., whether implementations fail on the same test cases), complemented by N-version reliability analysis under majority voting and manual inspection of the generated code. Structural diversity analysis shows that implementations from the same model are highly similar, while different models produce more distinct solutions. The same trend appears in behavioral diversity, with implementations from different models showing higher diversity yet still failing on the same tests far more often than expected under independence. N-version reliability analysis reinforces this: three- and five-version ensembles realize only 0.43 and 0.44 of the reliability gain achievable under independence, dropping below 0.3 when ensembles are built from the same model. Manual fault analysis shows that even different failure patterns often share root causes. Overall, these results suggest LLM-generated solutions do not satisfy NVP's failure independence assumption, though heterogeneous models help partially. They also validate our methodology as a tool for systematically evaluating failure independence as models evolve.

cs.SE↗

arXiv2025

Towards the Assessment of Task-based Chatbots: From the TOFU-R Snapshot to the BRASATO Curated Dataset

Task-based chatbots are increasingly being used to deliver real services, yet assessing their reliability, security, and robustness remains underexplored, also due to the lack of large-scale, high-quality datasets. The emerging automated quality assessment techniques targeting chatbots often rely on limited pools of subjects, such as custom-made toy examples, or outdated, no longer available, or scarcely popular agents, complicating the evaluation of such techniques. In this paper, we present two datasets and the tool support necessary to create and maintain these datasets. The first dataset is RASA TASK-BASED CHATBOTS FROM GITHUB (TOFU-R), which is a snapshot of the Rasa chatbots available on GitHub, representing the state of the practice in open-source chatbot development with Rasa. The second dataset is BOT RASA COLLECTION (BRASATO), a curated selection of the most relevant chatbots for dialogue complexity, functional complexity, and utility, whose goal is to ease reproducibility and facilitate research on chatbot reliability.

cs.SE↗