Clean Code In Practice: Challenges and Opportunities

• URL: http://arxiv.org/abs/2507.19721v1
• Date: Sat, 26 Jul 2025 00:13:50 GMT
• Title: Clean Code In Practice: Challenges and Opportunities
• Authors: Dapeng Yan, Wenjie Yang, Kui Liu, Zhiming Liu, Zhikuang Cai,
• Abstract summary: This paper explores the interplay between software reliability, safety, and security.<n>We identify critical threats to software reliability and provide a threat estimation framework.<n>We propose a set of actionable guidelines for practitioners to improve their reliability prediction models.
• Score: 6.520228635709776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliability prediction is crucial for ensuring the safety and security of software systems, especially in the context of industry practices. While various metrics and measurements are employed to assess software reliability, the complexity of modern systems necessitates a deeper understanding of how these metrics interact with security and safety concerns. This paper explores the interplay between software reliability, safety, and security, offering a comprehensive analysis of key metrics and measurement techniques used in the industry for reliability prediction. We identify critical threats to software reliability and provide a threat estimation framework that incorporates both safety and security aspects. Our findings suggest that integrating reliability metrics with safety and security considerations can enhance the robustness of software systems. Furthermore, we propose a set of actionable guidelines for practitioners to improve their reliability prediction models while simultaneously addressing the security and safety challenges of contemporary software applications.

Related papers

• Security Debt in Practice: Nuanced Insights from Practitioners [0.3277163122167433]
  Tight deadlines, limited resources, and prioritization of functionality over security can lead to insecure coding practices.<n>Despite their critical importance, there is limited empirical evidence on how software practitioners perceive, manage, and communicate Security Debts.<n>This study is based on semi-structured interviews with 22 software practitioners across various roles, organizations, and countries.
  arXiv  Detail & Related papers  (2025-07-15T14:28:28Z)
• Towards provable probabilistic safety for scalable embodied AI systems [79.31011047593492]
  Embodied AI systems are increasingly prevalent across various applications.<n> Ensuring their safety in complex operating environments remains a major challenge.<n>This Perspective offers a pathway toward safer, large-scale adoption of embodied AI systems in safety-critical applications.
  arXiv  Detail & Related papers  (2025-06-05T15:46:25Z)
• Towards Trustworthy GUI Agents: A Survey [64.6445117343499]
  This survey examines the trustworthiness of GUI agents in five critical dimensions.<n>We identify major challenges such as vulnerability to adversarial attacks, cascading failure modes in sequential decision-making.<n>As GUI agents become more widespread, establishing robust safety standards and responsible development practices is essential.
  arXiv  Detail & Related papers  (2025-03-30T13:26:00Z)
• AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement [73.0700818105842]
  We introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety.<n> AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques.<n>We conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness.
  arXiv  Detail & Related papers  (2025-02-24T02:11:52Z)
• Leveraging Security Observability to Strengthen Security of Digital Ecosystem Architecture [0.0]
  complexity poses significant challenges for both observability and security in a digital ecosystem.<n>Observability allows organizations to diagnose performance issues and detect anomalies in real time.<n>Security is focused on protecting sensitive data and ensuring service integrity.<n>This paper examines the interconnections between observability and security within digital ecosystem architectures.
  arXiv  Detail & Related papers  (2024-12-07T11:17:29Z)
• The Last Decade in Review: Tracing the Evolution of Safety Assurance Cases through a Comprehensive Bibliometric Analysis [7.431812376079826]
  Safety assurance is of paramount importance across various domains, including automotive, aerospace, and nuclear energy. The use of safety assurance cases allows for verifying the correctness of the created systems capabilities, preventing system failure.
  arXiv  Detail & Related papers  (2023-11-13T17:34:23Z)
• Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process [51.42800587382228]
  Safety assurance cases (SACs) can be challenging to maintain during system evolution. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
  arXiv  Detail & Related papers  (2023-07-14T16:03:27Z)
• Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements [76.80453043969209]
  This survey presents a framework for safety research pertaining to large models. We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models. We explore the strategies for enhancing large model safety from training to deployment.
  arXiv  Detail & Related papers  (2023-02-18T09:32:55Z)
• Towards an Improved Understanding of Software Vulnerability Assessment Using Data-Driven Approaches [0.0]
  The thesis advances the field of software security by providing knowledge and automation support for software vulnerability assessment. The key contributions include a systematisation of knowledge, along with a suite of novel data-driven techniques.
  arXiv  Detail & Related papers  (2022-07-24T10:22:28Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.