Mitigating Configuration Differences Between Development and Production Environments: A Catalog of Strategies
- URL: http://arxiv.org/abs/2505.09392v2
- Date: Thu, 15 May 2025 11:14:38 GMT
- Title: Mitigating Configuration Differences Between Development and Production Environments: A Catalog of Strategies
- Authors: Marcos Nazario, Rodrigo Bonifacio, Gustavo Pinto,
- Abstract summary: This study investigates the strategies software companies employ to mitigate the configuration differences between the development and production environments.<n>Our goal is to provide a comprehensive understanding of these strategies used to contribute to reducing the risk of configuration-related issues.
- Score: 2.6585985828583305
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Context: The Configuration Management of the development and production environments is an important aspect of IT operations. However, managing the configuration differences between these two environments can be challenging, leading to inconsistent behavior, unexpected errors, and increased downtime. Objective: In this study, we sought to investigate the strategies software companies employ to mitigate the configuration differences between the development and production environments. Our goal is to provide a comprehensive understanding of these strategies used to contribute to reducing the risk of configuration-related issues. Method: To achieve this goal, we interviewed 17 participants and leveraged the Thematic Analysis methodology to analyze the interview data. These participants shed some light on the current practices, processes, challenges, or issues they have encountered. Results: Based on the interviews, we systematically formulated and structured a catalog of eight strategies that explain how software producing companies mitigate these configuration differences. These strategies vary from 1) creating detailed configuration management plans, 2) using automation tools, and 3) developing processes to test and validate changes through containers and virtualization technologies. Conclusion: By implementing these strategies, companies can improve their ability to respond quickly and effectively to changes in the production environment. In addition, they can also ensure compliance with industry standards and regulations.
Related papers
- Towards AI Search Paradigm [42.62890561623222]
We introduce the AI Search Paradigm, a blueprint for next-generation search systems capable of emulating human information processing and decision-making.<n>The paradigm employs a modular architecture of four LLM-powered agents that dynamically adapt to the full spectrum of information needs.<n>By providing an in-depth guide to these components, this work aims to inform the development of trustworthy, adaptive, and scalable AI search systems.
arXiv Detail & Related papers (2025-06-20T17:42:13Z) - ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research [53.736407871322314]
We introduce ORMind, a cognitive-inspired framework that enhances optimization through counterfactual reasoning.<n>Our approach emulates human cognition, implementing an end-to-end workflow that transforms requirements into mathematical models and executable code.<n>It is currently being tested internally in Lenovo's AI Assistant, with plans to enhance optimization capabilities for both business and consumer customers.
arXiv Detail & Related papers (2025-06-02T05:11:21Z) - PATS: Process-Level Adaptive Thinking Mode Switching [53.53401063490537]
Current large-language models (LLMs) typically adopt a fixed reasoning strategy, either simple or complex, for all questions, regardless of their difficulty.<n>This neglect of variation in task and reasoning process complexity leads to an imbalance between performance and efficiency.<n>Existing methods attempt to implement training-free fast-slow thinking system switching to handle problems of varying difficulty, but are limited by coarse-grained solution-level strategy adjustments.<n>We propose a novel reasoning paradigm: Process-Level Adaptive Thinking Mode Switching (PATS), which enables LLMs to dynamically adjust their reasoning strategy based on the difficulty of each step, optimizing the balance between
arXiv Detail & Related papers (2025-05-25T17:58:50Z) - Adversarial Testing in LLMs: Insights into Decision-Making Vulnerabilities [5.0778942095543576]
This paper introduces an adversarial evaluation framework designed to systematically stress-test the decision-making processes of Large Language Models.<n>We apply this framework to several state-of-the-art LLMs, including GPT-3.5, GPT-4, Gemini-1.5, and DeepSeek-V3.<n>Our findings highlight distinct behavioral patterns across models and emphasize the importance of adaptability and fairness recognition for trustworthy AI deployment.
arXiv Detail & Related papers (2025-05-19T14:50:44Z) - A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making.<n>With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems.<n>We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
arXiv Detail & Related papers (2025-04-12T01:27:49Z) - What is a Feature, Really? Toward a Unified Understanding Across SE Disciplines [0.7125007887148752]
In software engineering, the concept of a feature'' is inconsistently defined across disciplines such as requirements engineering (RE) and software product lines (SPL)<n>This paper proposes an empirical, data-driven approach to explore how features are described, implemented, and managed across real-world projects.
arXiv Detail & Related papers (2025-02-14T09:08:53Z) - How Developers Choose Debugging Strategies for Challenging Web Application Defects [9.00716644826864]
This study investigates the factors influencing strategy choice in complex scenarios.<n>We found that contextual factors interact in complex ways, and combinations of factors influence strategy choice.<n>Our results show a gap between learning and effectively practicing strategies in challenging contexts.
arXiv Detail & Related papers (2025-01-20T23:43:36Z) - On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability [59.72892401927283]
We evaluate the planning capabilities of OpenAI's o1 models across a variety of benchmark tasks.
Our results reveal that o1-preview outperforms GPT-4 in adhering to task constraints.
arXiv Detail & Related papers (2024-09-30T03:58:43Z) - Defining Requirements Strategies in Agile: A Design Science Research Study [4.110602799032192]
Research shows that many of the challenges currently encountered with agile development are related to requirements engineering.
This paper investigates critical challenges that arise in agile development from an undefined requirements strategy.
arXiv Detail & Related papers (2024-05-29T07:57:32Z) - Microservice API Evolution in Practice: A Study on Strategies and
Challenges [45.085830389820956]
loose coupling poses new challenges to the API evolution process.
We conducted 17 semi-structured interviews with developers, architects, and managers in 11 companies.
We identified six strategies and six challenges for REpresentational State Transfer (REST) and event-driven communication via message brokers.
arXiv Detail & Related papers (2023-11-14T14:04:17Z) - On strategies for risk management and decision making under uncertainty shared across multiple fields [55.2480439325792]
The paper finds more than 110 examples of such strategies and this approach to risk is termed RDOT: Risk-reducing Design and Operations Toolkit.<n>RDOT strategies fall into six broad categories: structural, reactive, formal, adversarial, multi-stage and positive.<n>Overall, RDOT represents an overlooked class of versatile responses to uncertainty.
arXiv Detail & Related papers (2023-09-06T16:14:32Z) - A Methodology and Software Architecture to Support
Explainability-by-Design [0.0]
This paper describes Explainability-by-Design, a holistic methodology characterised by proactive measures to include explanation capability in the design of decision-making systems.
The methodology consists of three phases: (A) Explanation Requirement Analysis, (B) Explanation Technical Design, and (C) Explanation Validation.
It was shown that the approach is tractable in terms of development time, which can be as low as two hours per sentence.
arXiv Detail & Related papers (2022-06-13T15:34:29Z) - Policy Architectures for Compositional Generalization in Control [71.61675703776628]
We introduce a framework for modeling entity-based compositional structure in tasks.
Our policies are flexible and can be trained end-to-end without requiring any action primitives.
arXiv Detail & Related papers (2022-03-10T06:44:24Z) - Learning Adaptive Exploration Strategies in Dynamic Environments Through
Informed Policy Regularization [100.72335252255989]
We study the problem of learning exploration-exploitation strategies that effectively adapt to dynamic environments.
We propose a novel algorithm that regularizes the training of an RNN-based policy using informed policies trained to maximize the reward in each task.
arXiv Detail & Related papers (2020-05-06T16:14:48Z)
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.