Related papers: Leveraging Sustainable Systematic Literature Reviews

Leveraging Sustainable Systematic Literature Reviews

URL: http://arxiv.org/abs/2501.01819v1
Date: Fri, 03 Jan 2025 14:03:15 GMT
Title: Leveraging Sustainable Systematic Literature Reviews
Authors: Vinicius dos Santos, Rick Kazman, Elisa Yumi Nakagawa,
Abstract summary: This paper presents concrete directions towards sustainable SLRs.<n>We first identified 18 green drivers'' (GD) that could directly impact SLR sustainability.<n>We distilled 25 sustainability indicators (SI) associated with the GD to assess SLRs regarding their sustainability.
Score: 8.18445480530188
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Systematic Literature Reviews (SLRs) are a widely employed research method in software engineering. However, there are several problems with SLRs, including the enormous time and effort to conduct them and the lack of obvious impacts of SLR results on software engineering practices and industry projects. To address these problems, the concepts of \textit{sustainability} and \textit{sustainable SLR} have been proposed, aiming to raise awareness among researchers about the importance of dealing with SLR problems in a consistent way; however, practical and concrete actions are still lacking. This paper presents concrete directions towards sustainable SLRs. We first identified 18 ``green drivers'' (GD) that could directly impact SLR sustainability, and we distilled 25 sustainability indicators (SI) associated with the GD to assess SLRs regarding their sustainability. A preliminary evaluation was conducted on the ten top-cited SLRs in software engineering published over the last decade. From this analysis, we synthesized our insights into 12 leverage points for sustainability. Our results indicate that even in high-quality reviews, there are threats to sustainability, such as: flaws in the search process, lack of essential details in the documentation, weak collaboration with stakeholders, poor knowledge management, lack of use of supporting tools, and a dearth of practical insights for software engineering practitioners. The good news is that moving towards sustainable SLRs only requires some simple actions, which can pave the way for a profound change in the software engineering community's mindset about how to create and sustain SLRs.

Related papers

A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond [88.5807076505261]
Large Reasoning Models (LRMs) have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference. A growing concern lies in their tendency to produce excessively long reasoning traces. This inefficiency introduces significant challenges for training, inference, and real-world deployment.
arXiv Detail & Related papers (2025-03-27T15:36:30Z)
LLM-Safety Evaluations Lack Robustness [58.334290876531036]
We argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of noise. We propose a set of guidelines for reducing noise and bias in evaluations of future attack and defense papers.
arXiv Detail & Related papers (2025-03-04T12:55:07Z)
Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives [52.863024096759816]
Misaligned research objectives have hindered progress in adversarial robustness research over the past decade. We argue that realigned objectives are necessary for meaningful progress in adversarial alignment.
arXiv Detail & Related papers (2025-02-17T15:28:40Z)
Critic-CoT: Boosting the reasoning abilities of large language model via Chain-of-thoughts Critic [48.94340387130627]
Critic-CoT is a framework that pushes LLMs toward System-2-like critic capability. CoT reasoning paradigm and the automatic construction of distant-supervision data without human annotation. Experiments on GSM8K and MATH demonstrate that our enhanced model significantly boosts task-solving performance.
arXiv Detail & Related papers (2024-08-29T08:02:09Z)
Exploring Automatic Cryptographic API Misuse Detection in the Era of LLMs [60.32717556756674]
This paper introduces a systematic evaluation framework to assess Large Language Models in detecting cryptographic misuses. Our in-depth analysis of 11,940 LLM-generated reports highlights that the inherent instabilities in LLMs can lead to over half of the reports being false positives. The optimized approach achieves a remarkable detection rate of nearly 90%, surpassing traditional methods and uncovering previously unknown misuses in established benchmarks.
arXiv Detail & Related papers (2024-07-23T15:31:26Z)
MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
arXiv Detail & Related papers (2024-06-20T03:50:23Z)
Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents [101.17919953243107]
GovSim is a generative simulation platform designed to study strategic interactions and cooperative decision-making in large language models (LLMs)<n>We find that all but the most powerful LLM agents fail to achieve a sustainable equilibrium in GovSim, with the highest survival rate below 54%.<n>We show that agents that leverage "Universalization"-based reasoning, a theory of moral thinking, are able to achieve significantly better sustainability.
arXiv Detail & Related papers (2024-04-25T15:59:16Z)
Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs [10.241642683713467]
Large-language models (LLMs) have shown significant promise in diverse domains, including natural language processing, code generation, and program understanding. This chapter explores the potential of LLMs in driving Requirements Engineering processes, aiming to improve the efficiency and accuracy of requirements-related tasks.
arXiv Detail & Related papers (2023-10-21T11:29:31Z)
CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools [10.653984116770234]
ChatReport is a novel LLM-based system to automate the analysis of corporate sustainability reports. We make our methodology, annotated datasets, and generated analyses of 1015 reports publicly available.
arXiv Detail & Related papers (2023-07-28T18:58:16Z)
Sentiment Analysis in the Era of Large Language Models: A Reality Check [69.97942065617664]
This paper investigates the capabilities of large language models (LLMs) in performing various sentiment analysis tasks. We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets.
arXiv Detail & Related papers (2023-05-24T10:45:25Z)
A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets [0.0]
SLR usage has increased in many applications, but the environment, background image resolution, modalities, and datasets affect the performance a lot. This review paper facilitates a comprehensive overview of SLR and discusses the needs, challenges, and problems associated with SLR. Research progress and existing state-of-the-art SLR models over the past decade have been reviewed.
arXiv Detail & Related papers (2022-04-07T09:49:12Z)
On how Cognitive Computing will plan your next Systematic Review [3.0816257225447763]
We report on the insights from 24 SLR authors on planning practices, its challenges and feedback on support strategies. We frame our findings under the cognitive augmentation framework, and report on a prototype implementation and evaluation.
arXiv Detail & Related papers (2020-12-15T09:56:09Z)

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