Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration

• URL: http://arxiv.org/abs/2406.19765v2
• Date: Tue, 2 Jul 2024 08:29:56 GMT
• Title: Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration
• Authors: Ali Kazemi Arani, Triet Huynh Minh Le, Mansooreh Zahedi, M. Ali Babar,
• Abstract summary: Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights in specific phases. The rise of continuous practices in software projects emphasizes automating Continuous Integration (CI) with these learning-based methods. This study provides a comprehensive overview of learning-based methods in CI, offering valuable insights for researchers and practitioners developing CI task automation.
• Score: 2.3436632098950456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context: Machine learning (ML) and deep learning (DL) analyze raw data to extract valuable insights in specific phases. The rise of continuous practices in software projects emphasizes automating Continuous Integration (CI) with these learning-based methods, while the growing adoption of such approaches underscores the need for systematizing knowledge. Objective: Our objective is to comprehensively review and analyze existing literature concerning learning-based methods within the CI domain. We endeavour to identify and analyse various techniques documented in the literature, emphasizing the fundamental attributes of training phases within learning-based solutions in the context of CI. Method: We conducted a Systematic Literature Review (SLR) involving 52 primary studies. Through statistical and thematic analyses, we explored the correlations between CI tasks and the training phases of learning-based methodologies across the selected studies, encompassing a spectrum from data engineering techniques to evaluation metrics. Results: This paper presents an analysis of the automation of CI tasks utilizing learning-based methods. We identify and analyze nine types of data sources, four steps in data preparation, four feature types, nine subsets of data features, five approaches for hyperparameter selection and tuning, and fifteen evaluation metrics. Furthermore, we discuss the latest techniques employed, existing gaps in CI task automation, and the characteristics of the utilized learning-based techniques. Conclusion: This study provides a comprehensive overview of learning-based methods in CI, offering valuable insights for researchers and practitioners developing CI task automation. It also highlights the need for further research to advance these methods in CI.

Related papers

• Online Continual Learning: A Systematic Literature Review of Approaches, Challenges, and Benchmarks [1.3631535881390204]
  Online Continual Learning (OCL) is a critical area in machine learning. This study conducts the first comprehensive Systematic Literature Review on OCL.
  arXiv  Detail & Related papers  (2025-01-09T01:03:14Z)
• Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting [7.081184240581488]
  This article presents a study that aims to address these challenges by enhancing the efficiency and scope of systematic reviews in the social sciences through advanced machine learning (ML) and natural language processing (NLP) tools. In particular, we focus on automating stages within the systematic reviewing process that are time-intensive and repetitive for human annotators and which lend themselves to immediate scalability through tools such as information retrieval and summarisation guided by expert advice. The article concludes with a summary of lessons learnt regarding the integrated approach towards systematic reviews and future directions for improvement, including explainability.
  arXiv  Detail & Related papers  (2024-12-11T17:54:01Z)
• Large Language Model for Qualitative Research -- A Systematic Mapping Study [3.302912592091359]
  Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools. This study systematically maps the literature on the use of LLMs for qualitative research. Findings reveal that LLMs are utilized across diverse fields, demonstrating the potential to automate processes.
  arXiv  Detail & Related papers  (2024-11-18T21:28:00Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• A Large Language Model Approach to Educational Survey Feedback Analysis [0.0]
  This paper assesses the potential for the large language models (LLMs) GPT-4 and GPT-3.5 to aid in deriving insight from education feedback surveys.
  arXiv  Detail & Related papers  (2023-09-29T17:57:23Z)
• Systematic Literature Review on Application of Machine Learning in Continuous Integration [7.180264400668846]
  This research conducted a systematic review of the literature on machine learning (ML)-based methods in the context of Continuous Integration (CI) over the past 22 years. The study aimed to identify and describe the techniques used in ML-based solutions for CI.
  arXiv  Detail & Related papers  (2023-05-22T04:07:36Z)
• Latent Properties of Lifelong Learning Systems [59.50307752165016]
  We introduce an algorithm-agnostic explainable surrogate-modeling approach to estimate latent properties of lifelong learning algorithms. We validate the approach for estimating these properties via experiments on synthetic data.
  arXiv  Detail & Related papers  (2022-07-28T20:58:13Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research [69.48907856390834]
  Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot. This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
  arXiv  Detail & Related papers  (2021-10-14T15:14:33Z)
• An Extensible Benchmark Suite for Learning to Simulate Physical Systems [60.249111272844374]
  We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols. We propose four representative physical systems, as well as a collection of both widely used classical time-based and representative data-driven methods.
  arXiv  Detail & Related papers  (2021-08-09T17:39:09Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)

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.