🧠Introduction

Definition: A systematic literature review is a summary, analysis, and evaluation of all the existing research on a well-formulated and specific question.

A systematic literature review (SLR) identifies, selects and critically appraises research in order to answer a clearly formulated question (Dewey, A. & Drahota, A. 2016). The systematic review should follow a clearly defined protocol or plan where the criteria is clearly stated before the review is conducted.

A systematic review is the highest form of knowledge in science

SLR in Computer Science Research

Systematic Literature Reviews (SLRs) are valuable in computer science research in several situations:

1. Identifying Existing Knowledge: SLRs help researchers identify and synthesize the existing body of knowledge on a particular topic. In computer science, where technology evolves rapidly, it's essential to stay updated on the latest research and understand the state of the art.

2. Avoiding Redundancy: Without a systematic review, researchers may inadvertently duplicate existing work. An SLR ensures that the research is novel and doesn't replicate previous efforts.

3. Informed Decision-Making: SLRs provide a comprehensive view of the field, enabling researchers to make informed decisions about research directions, methodologies, and areas that need further exploration.

4. Quality Assurance: Through a rigorous process of study selection and evaluation, SLRs ensure that only high-quality research is included. This helps in maintaining the quality and credibility of the research.

5. Evidence-Based Research: SLRs provide a solid foundation of evidence upon which new research can be built. This evidence-based approach enhances the rigor and validity of computer science research.

6. Establishing a Research Baseline: Use SLRs at the beginning of a research project to gain a comprehensive understanding of the existing knowledge and literature related to your research topic. This helps identify gaps and informs the direction of your research.

7. Technology Assessment: When you're considering the adoption of a new technology, framework, or methodology in computer science, conduct an SLR to assess its feasibility, advantages, and limitations based on existing literature.

8. Methodology Selection: Before embarking on a research project, especially when dealing with complex computational methods or algorithms, perform an SLR to compare and select the most appropriate research methodologies or techniques.

9. Benchmarking and Performance Evaluation: When developing computer algorithms, software, or systems, use SLRs to collect benchmark datasets and performance metrics from existing research to establish a baseline for your own work and facilitate meaningful comparisons.

10. Identifying Security Threats: In cybersecurity and network security research, SLRs help in identifying known vulnerabilities, attack vectors, and defense mechanisms. This is crucial for building secure systems or developing intrusion detection systems.

11. Quality Assurance: When conducting empirical studies or experiments, refer to SLRs to ensure that your research design aligns with established best practices, ensuring the validity and reliability of your results.

12. Policy and Regulation Compliance: If your computer science research involves data privacy, ethics, or compliance with regulations like GDPR, use SLRs to understand the legal and ethical landscape and how it applies to your work.

13. Informed Decision-Making: SLRs are useful for decision-making in software engineering and project management. They provide insights into best practices, tools, and approaches used by others in similar projects.

14. Trend Analysis: To stay up-to-date with emerging trends and technologies in computer science, conduct periodic SLRs to monitor the evolution of a particular field or subfield.

15. Thesis and Dissertation Preparation: When writing a thesis or dissertation in computer science, an SLR can serve as a comprehensive literature review section, providing the groundwork for your research context and objectives.

SLRs are essential in computer science research when you need to establish a knowledge baseline, make informed decisions, identify gaps, validate your research methods, or ensure compliance with best practices and standards. They help researchers stay current with the ever-evolving field of computer science and contribute to the rigor and validity of their work.

What is a systematic review?

Table 1: Types of Systematic Reviews

Review Type	Summary
Mapping Review/Systematic Map	A mapping review maps existing literature and categorizes data. It characterizes the quantity and quality of literature, often by study design, and can identify the need for further research.
Meta-analysis	Meta-analysis combines the results of multiple quantitative studies using statistical methods to provide evidence from multiple studies. Data sources can be individual participant data or aggregate data (e.g., odds ratios).
Mixed Studies Review/Mixed Methods Review	A review that combines different research methods, often with a systematic literature review as a significant stage. It can involve combining quantitative and qualitative research approaches.
Qualitative Systematic Review	This method integrates or compares findings from qualitative studies, often involving data coding and identifying themes or constructs across studies. Multiple authors may improve data validity.
Rapid Review	Rapid reviews assess existing knowledge about a policy or practice issue, using systematic review methods but with some simplifications or omissions for speed. These were utilized during the COVID-19 pandemic.
Systematic Review	A systematic search for data using a repeatable method. It includes data appraisal (e.g., quality assessment) and synthesis of research data.
Systematic Search and Review	Combines critical review methods with a comprehensive search process. Typically used for broad questions to produce appropriate evidence synthesis, may or may not include quality assessments.
Systematized Review	Incorporates elements of the systematic review process but often with less comprehensive searching and may omit quality assessments of data sources.

Case Study: Improving Web Security through SLR

Background: A team of computer science researchers is interested in improving web security. They want to develop a new security mechanism but want to ensure it's both innovative and grounded in existing knowledge.

Importance of SLR:

1. Understanding Current Approaches: The researchers start by conducting an SLR to understand the current state of web security in computer science. They collect and analyze existing research papers, articles, and reports on web security.

2. Identifying Gaps: Through the SLR, the researchers identify several gaps in the existing literature. They find that while there are many studies on specific aspects of web security (e.g., encryption, authentication), there is a lack of comprehensive research that integrates these aspects into a holistic security framework.

3. Validating the Need for Innovation: The SLR reveals that the current web security solutions have limitations and vulnerabilities. This validates the need for the researchers' proposed security mechanism.

4. Informed Design: Armed with the insights from the SLR, the researchers design their security mechanism. They incorporate the best practices and lessons learned from existing literature, ensuring that their solution addresses the identified gaps and vulnerabilities.

5. Enhanced Credibility: When the researchers publish their work, they can reference the SLR in their introduction to establish the context and need for their research. This enhances the credibility of their work, as it's based on a comprehensive review of existing knowledge.

The systematic literature review played a pivotal role in guiding the researchers' work, ensuring that their new web security mechanism was well-informed, innovative, and aligned with the current state of computer science research in the field of web security.

Others Case Studies:

Systematic Literature Reviews (SLRs) are valuable in various scenarios within computer science research. Here are 10 case studies illustrating when SLRs should be used:

Case Study	Scenario	Use of SLR
1. Identifying Trends in AI Research	Researchers aim to understand the emerging trends in artificial intelligence (AI).	Conducting an SLR on recent AI literature to identify popular techniques, application domains, and challenges.
2. Evaluating Software Testing Methods	A software development team wants to improve their testing process.	Performing an SLR on software testing methodologies to determine which approaches are most effective in a particular context.
3. Assessing Cybersecurity Threats	An organization needs to strengthen its cybersecurity measures.	Conducting an SLR on cybersecurity threat reports to understand common vulnerabilities and attack patterns.
4. Optimizing Database Query Performance	A database administrator wants to enhance query performance.	Conducting an SLR on database optimization techniques to identify best practices and performance-enhancing strategies.
5. Improving User Experience in Mobile Apps	Mobile app developers aim to enhance user experience.	Conducting an SLR on user interface design and usability studies to identify principles for mobile app design.
6. Enhancing Natural Language Processing (NLP) Models	NLP researchers seek to improve language understanding models.	Conducting an SLR on recent NLP literature to identify state-of-the-art models, datasets, and evaluation metrics.
7. Enhancing Software Development Productivity	A software development team wants to adopt agile practices.	Conducting an SLR on agile software development methodologies to assess their impact on productivity.
8. Developing Autonomous Robotics Algorithms	Researchers are designing autonomous robots for various applications.	Conducting an SLR on robotics algorithms to gather insights into navigation, perception, and control techniques.
9. Securing Internet of Things (IoT) Devices	IoT device manufacturers aim to enhance security.	Conducting an SLR on IoT security literature to identify vulnerabilities and best practices.
10. Enhancing Big Data Analytics	Data scientists want to improve their big data analysis methods.	Conducting an SLR on big data analytics techniques to determine which algorithms and tools are most effective for specific tasks.

In each of these case studies, conducting a systematic literature review is critical to ensuring that decisions and research directions are grounded in existing knowledge, best practices, and the current state of the field in computer science. SLRs provide a structured and evidence-based approach to gather and synthesize relevant information, making them invaluable in these scenarios.