Smells-sus: Sustainability Smells in IaC
- URL: http://arxiv.org/abs/2501.07676v2
- Date: Mon, 20 Jan 2025 20:05:35 GMT
- Title: Smells-sus: Sustainability Smells in IaC
- Abstract summary: This research focuses on Terraform, a widely adopted Infrastructure as Code (IaC) tool.<n>We define seven sustainability smells and validate them through a survey with 19 IaC practitioners.<n>The most prevalent smell is Monolithic Infrastructure, which appears in 9.67% of the scripts.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Practitioners use Infrastructure as Code (IaC) scripts to efficiently configure IT infrastructures through machine-readable definition files. However, during the development of these scripts, some code patterns or deployment choices may lead to sustainability issues like inefficient resource utilization or redundant provisioning for example. We call this type of patterns sustainability smells. These inefficiencies pose significant environmental and financial challenges, given the growing scale of cloud computing. This research focuses on Terraform, a widely adopted IaC tool. Our study involves defining seven sustainability smells and validating them through a survey with 19 IaC practitioners. We utilized a dataset of 28,327 Terraform scripts from 395 open-source repositories. We performed a detailed qualitative analysis of a randomly sampled 1,860 Terraform scripts from the original dataset to identify code patterns that correspond to the sustainability smells and used the other 26,467 Terraform scripts to study the prevalence of the defined sustainability smells. Our results indicate varying prevalence rates of these smells across the dataset. The most prevalent smell is Monolithic Infrastructure, which appears in 9.67\% of the scripts. Additionally, our findings highlight the complexity of conducting root cause analysis for sustainability issues, as these smells often arise from a confluence of script structures, configuration choices, and deployment contexts.
Related papers
- Walk Before You Run: The Importance of Data Exploration for Data Analysis Agents [7.140798105546584]
In current tools and benchmarks, this step is usually left implicit, creating a gap between downstream task performance and dataset understanding.<n>We show through downstream experiments that stronger Data Exploration support improves task performance.<n>Our results show that strong LLMs and data-analysis agents still miss important logical structure even when they read spreadsheet content.
arXiv Detail & Related papers (2026-08-17T03:19:59Z) - SWE-InfraBench: Evaluating Language Models on Cloud Infrastructure Code [0.6695324715259999]
Building infrastructure-as-code (IaC) in cloud computing is a critical task.<n>Our benchmark reflects the incremental code edits common in enterprise development with imperative tools like the AWS CDK.<n>We present SWE-InfraBench, a diverse evaluation dataset sourced from dozens of real-world IaCs.
arXiv Detail & Related papers (2026-06-03T12:48:53Z) - Agentic Insight Generation in VSM Simulations [4.626393563516877]
We propose a decoupled, two-step agentic architecture for value stream map simulations.<n>By separating orchestration from data analysis, the system leverages progressive data discovery infused with domain expert knowledge.<n>Results from multiple state-of-the-art large language models demonstrate the framework's viability.
arXiv Detail & Related papers (2026-04-14T08:11:53Z) - Dynamic analysis enhances issue resolution [53.50448142467294]
DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
arXiv Detail & Related papers (2026-03-23T14:48:54Z) - A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives [0.0]
PANGAEA-GPT is a hierarchical multi-agent framework designed for autonomous data discovery and analysis.<n>Unlike standard Large Language Model (LLM) wrappers, our architecture implements a centralized Supervisor-Worker topology.<n>We demonstrate the system's capacity to execute complex, multi-step deterministic runtime with minimal human intervention.
arXiv Detail & Related papers (2026-02-24T20:37:38Z) - FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents [53.03492387564392]
We introduce FS-Researcher, a file-system-based framework that scales deep research beyond the context window via a persistent workspace.<n>A Context Builder agent browses the internet, writes structured notes, and archives raw sources into a hierarchical knowledge base that can grow far beyond context length.<n>A Report Writer agent then composes the final report section by section, treating the knowledge base as the source of facts.
arXiv Detail & Related papers (2026-02-02T03:00:19Z) - Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers [103.4410890572479]
We introduce the Loong Project: an open-source framework for scalable synthetic data generation and verification.<n>LoongBench is a curated seed dataset containing 8,729 human-vetted examples across 12 domains.<n>LoongEnv is a modular synthetic data generation environment that supports multiple prompting strategies to produce new question-answer-code triples.
arXiv Detail & Related papers (2025-09-03T06:42:40Z) - StructText: A Synthetic Table-to-Text Approach for Benchmark Generation with Multi-Dimensional Evaluation [8.251302684712773]
StructText is an end-to-end framework for automatically generating high-fidelity benchmarks for key-value extraction from text.<n>We evaluate the proposed method on 71,539 examples across 49 documents.
arXiv Detail & Related papers (2025-07-28T21:20:44Z) - Benchmarking Deep Search over Heterogeneous Enterprise Data [73.55304268238474]
We present a new benchmark for evaluating a form of retrieval-augmented generation (RAG)<n>RAG requires source-aware, multi-hop reasoning over diverse, sparsed, but related sources.<n>We build it using a synthetic data pipeline that simulates business across product planning, development, and support stages.
arXiv Detail & Related papers (2025-06-29T08:34:59Z) - DSR-Bench: Evaluating the Structural Reasoning Abilities of LLMs via Data Structures [20.596558700597644]
Large language models (LLMs) are increasingly deployed for real-world tasks that fundamentally involve data manipulation.<n>A core requirement is the ability to perform structural reasoning--that is, to understand and reason about data relationships.<n>We introduce DSR-Bench, a novel benchmark evaluating LLMs' structural reasoning capabilities through data structures.
arXiv Detail & Related papers (2025-05-29T23:24:53Z) - A Defect Taxonomy for Infrastructure as Code: A Replication Study [0.0]
Prior study developed such a taxonomy, but based it exclusively on the declarative language Puppet.<n>It remained unknown whether this taxonomy applies to programming language-based IaC tools like Pulumi, Terraform CDK, and AWS CDK.<n>We performed qualitative analysis on 3,364 defect-related commits from 285 open-source PL-IaC repositories.
arXiv Detail & Related papers (2025-05-02T20:18:55Z) - CISOL: An Open and Extensible Dataset for Table Structure Recognition in the Construction Industry [0.0]
This paper introduces the Construction Industry Steel Ordering List (CISOL) dataset.
CISOL provides a valuable new research resource and highlights the importance of having diverse datasets.
The dataset contains more than 120,000 annotated instances in over 800 document images.
arXiv Detail & Related papers (2025-01-26T10:16:21Z) - DSAI: Unbiased and Interpretable Latent Feature Extraction for Data-Centric AI [24.349800949355465]
Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets.<n>We propose Data Scientist AI (DSAI), a framework that enables unbiased and interpretable feature extraction.
arXiv Detail & Related papers (2024-12-09T08:47:05Z) - Querying Perception Streams with Spatial Regular Expressions [3.6814516646862683]
We introduce SpREs as a novel querying language for pattern matching over streams containing spatial and temporal data.
We develop the STREM tool as both an offline and online pattern matching framework for perception data.
Using our matching framework, we are able to find over 20,000 matches within 296 ms making STREM applicable in runtime monitoring applications.
arXiv Detail & Related papers (2024-11-08T20:15:27Z) - BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data [61.936320820180875]
Large language models (LLMs) have become increasingly pivotal across various domains.
BabelBench is an innovative benchmark framework that evaluates the proficiency of LLMs in managing multimodal multistructured data with code execution.
Our experimental findings on BabelBench indicate that even cutting-edge models like ChatGPT 4 exhibit substantial room for improvement.
arXiv Detail & Related papers (2024-10-01T15:11:24Z) - DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery.
Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering.
Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
arXiv Detail & Related papers (2024-07-01T18:58:22Z) - Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic [51.967603572656266]
We introduce a consistent and theoretically grounded approach to annotating decompositional entailment.
We find that our new dataset, RDTE, has a substantially higher internal consistency (+9%) than prior decompositional entailment datasets.
We also find that training an RDTE-oriented entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine significantly improves both accuracy and proof quality.
arXiv Detail & Related papers (2024-02-22T18:55:17Z) - DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain
Question Answering over Knowledge Base and Text [73.68051228972024]
Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge.
Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
arXiv Detail & Related papers (2023-10-31T04:37:57Z) - Enhancing Interpretability and Generalizability in Extended Isolation Forests [5.139809663513828]
Extended Isolation Forest Feature Importance (ExIFFI) is a method that explains predictions made by Extended Isolation Forest (EIF) models.
EIF+ is designed to enhance the model's ability to detect unseen anomalies through a revised splitting strategy.
ExIFFI outperforms other unsupervised interpretability methods on 8 of 11 real-world datasets.
arXiv Detail & Related papers (2023-10-09T07:24:04Z) - GAN-based Tabular Data Generator for Constructing Synopsis in
Approximate Query Processing: Challenges and Solutions [0.0]
Approximate Query Processing (AQP) is a technique for providing approximate answers to aggregate queries based on a summary of the data (synopsis)
This study explores the novel utilization of Generative Adversarial Networks (GANs) in the generation of tabular data that can be employed in AQP for synopsis construction.
Our findings demonstrate that advanced GAN variations exhibit a promising capacity to generate high-fidelity synopses, potentially transforming the efficiency and effectiveness of AQP in data-driven systems.
arXiv Detail & Related papers (2022-12-18T05:11:04Z) - TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual
Environments [84.6017003787244]
This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets.
We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
arXiv Detail & Related papers (2022-08-16T20:46:08Z) - Bridging the Gap Between Clean Data Training and Real-World Inference
for Spoken Language Understanding [76.89426311082927]
Existing models are trained on clean data, which causes a textitgap between clean data training and real-world inference.
We propose a method from the perspective of domain adaptation, by which both high- and low-quality samples are embedding into similar vector space.
Experiments on the widely-used dataset, Snips, and large scale in-house dataset (10 million training examples) demonstrate that this method not only outperforms the baseline models on real-world (noisy) corpus but also enhances the robustness, that is, it produces high-quality results under a noisy environment.
arXiv Detail & Related papers (2021-04-13T17:54:33Z) - Data Mining with Big Data in Intrusion Detection Systems: A Systematic
Literature Review [68.15472610671748]
Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation.
The rapid rate and volume of data creation has begun to pose significant challenges for data management and security.
The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance.
arXiv Detail & Related papers (2020-05-23T20:57:12Z)
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.