Secondary Studies in the Academic Context: A Systematic Mapping and Survey

• URL: http://arxiv.org/abs/2007.07751v1
• Date: Fri, 10 Jul 2020 20:01:26 GMT
• Title: Secondary Studies in the Academic Context: A Systematic Mapping and Survey
• Authors: Katia Romero Felizardo, \'Erica Ferreira de Souza, Bianca Minetto Napole\~ao, Nandamudi Lankalapalli Vijaykumar, Maria Teresa Baldassarre
• Abstract summary: The main goal of this study is to provide an overview on the use of secondary studies in an academic context. We conducted an SM to identify the available and relevant studies on the use of secondary studies as a research methodology for conducting SE research projects. Secondly, a survey was performed with 64 SE researchers to identify their perception related to the value of performing secondary studies to support their research projects.
• Score: 4.122293798697967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context: Several researchers have reported their experiences in applying secondary studies (Systematic Literature Reviews - SLRs and Systematic Mappings - SMs) in Software Engineering (SE). However, there is still a lack of studies discussing the value of performing secondary studies in an academic context. Goal: The main goal of this study is to provide an overview on the use of secondary studies in an academic context. Method: Two empirical research methods were used. Initially, we conducted an SM to identify the available and relevant studies on the use of secondary studies as a research methodology for conducting SE research projects. Secondly, a survey was performed with 64 SE researchers to identify their perception related to the value of performing secondary studies to support their research projects. Results: Our results show benefits of using secondary studies in the academic context, such as, providing an overview of the literature as well as identifying relevant research literature on a research area enabling to find reasons to explain why a research project should be approved for a grant and/or supporting decisions made in a research project. Difficulties faced by SE graduate students with secondary studies are that they tend to be conducted by a team and it demands more effort than a traditional review. Conclusions: Secondary studies are valuable to graduate students. They should consider conducting a secondary study for their research project due to the benefits and contributions provided to develop the overall project. However, the advice of an experienced supervisor is essential to avoid bias. In addition, the acquisition of skills can increase student's motivation to pursue their research projects and prepare them for both academic or industrial careers.

Related papers

• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently. Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• Software analytics for software engineering: A tertiary review [2.7386485828693576]
  We identify five secondary studies on the use of software analytics (SA) for software engineering (SE) Despite the overlapping objectives and search time frames of these secondary studies, there is negligible overlap of primary studies between these secondary studies. We conclude that an overview of the literature identified by these secondary studies would be useful in providing a more comprehensive overview of the topic.
  arXiv  Detail & Related papers  (2024-10-08T08:28:03Z)
• What is the Role of Large Language Models in the Evolution of Astronomy Research? [0.0]
  ChatGPT and other state-of-the-art large language models (LLMs) are rapidly transforming multiple fields. These models, commonly trained on vast datasets, exhibit human-like text generation capabilities.
  arXiv  Detail & Related papers  (2024-09-30T12:42:25Z)
• ResearchArena: Benchmarking Large Language Models' Ability to Collect and Organize Information as Research Agents [21.17856299966841]
  This study introduces ResearchArena, a benchmark designed to evaluate large language models (LLMs) in conducting academic surveys. To support these opportunities, we construct an environment of 12M full-text academic papers and 7.9K survey papers.
  arXiv  Detail & Related papers  (2024-06-13T03:26:30Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work. ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
  This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers. SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level. Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
  arXiv  Detail & Related papers  (2024-04-09T15:01:51Z)
• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [55.33653554387953]
  Pattern Analysis and Machine Intelligence (PAMI) has led to numerous literature reviews aimed at collecting and fragmented information. This paper presents a thorough analysis of these literature reviews within the PAMI field. We try to address three core research questions: (1) What are the prevalent structural and statistical characteristics of PAMI literature reviews; (2) What strategies can researchers employ to efficiently navigate the growing corpus of reviews; and (3) What are the advantages and limitations of AI-generated reviews compared to human-authored ones.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• Privacy Impact Assessments in the Wild: A Scoping Review [1.7677916783208343]
  Privacy Impact Assessments (PIAs) offer a systematic process for assessing the privacy impacts of a project or system. PIAs are heralded as one of the main approaches to privacy by design, supporting the early identification of threats and controls. There is still a significant need for more primary research on the topic, both qualitative and quantitative.
  arXiv  Detail & Related papers  (2024-02-17T05:07:10Z)
• Apples, Oranges, and Software Engineering: Study Selection Challenges for Secondary Research on Latent Variables [8.612556181934291]
  The inability to measure abstract concepts directly poses a challenge for secondary studies in software engineering. Standardized measurement instruments are rarely available, and even if they are, many researchers do not use them or do not even provide a definition for the studied concept. SE researchers conducting secondary studies therefore have to decide a) which primary studies intended to measure the same construct, and b) how to compare and aggregate vastly different measurements for the same construct.
  arXiv  Detail & Related papers  (2024-02-13T17:32:17Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• A Survey of Knowledge Tracing: Models, Variants, and Applications [70.69281873057619]
  Knowledge Tracing is one of the fundamental tasks for student behavioral data analysis. We present three types of fundamental KT models with distinct technical routes. We discuss potential directions for future research in this rapidly growing field.
  arXiv  Detail & Related papers  (2021-05-06T13:05:55Z)

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.