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A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research

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WorkDifferentWithAI.com Academic Paper Alert!

Written by Sicong Cao, Xiaobing Sun, Ratnadira Widyasari, David Lo, Xiaoxue Wu, Lili Bo, Jiale Zhang, Bin Li, Wei Liu, Di Wu, Yixin Chen

Category: “AI for IT”

Article Section: AI Development and Operations; Automated Code Refactoring

Publication Date: 2024-01-25

SEO Description: “Exploring AI explainability in software engineering: A comprehensive literature review across 63 studies.”

Cao, Sicong, et al. A Systematic Literature Review on Explainability for Machine/Deep Learning-Based Software Engineering Research. arXiv:2401.14617, arXiv, 25 Jan. 2024, https://doi.org/10.48550/arXiv.2401.14617.

Keywords

Explainability, Machine Learning, Deep Learning, Software Engineering, Systematic Literature Review

AI-Generated Paper Summary

Generated by Ethical AI Researcher GPT

Ethical AI Researcher Summary:

The paper titled “A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research” conducted by Sicong Cao and colleagues from various institutions including Yangzhou University, China, Singapore Management University, Singapore, University of Southern Queensland, Australia, and Washington University in St. Louis, USA, addresses the critical issue of explainability within AI-driven models employed in Software Engineering (SE) tasks. This research is motivated by the increasingly prevalent use of complex Machine Learning (ML) and Deep Learning (DL) algorithms in SE which, despite their impressive performance capabilities, suffer from a “black-box” nature, limiting their practical deployment due to a lack of transparency and explainability. The review meticulously analyzes 63 papers covering 21 unique SE tasks from major SE & AI conferences and journals, aiming to summarize where explainable AI (XAI) techniques have been successful, classify different XAI techniques, and inspect existing evaluation approaches. The study unfolds the challenges in current research and envisages a roadmap for future efforts in enhancing the explainability of AI models in SE.

The authors highlight the growing need for explainability in AI systems from both the user’s and designer’s perspective, emphasizing that the convoluted nature and large size of current models make understanding and trust a significant hurdle to the widespread adoption of these technologies in SE industries. Previous surveys are acknowledged while also pointing out the limitations therein, underscoring the necessity of a more comprehensive review that includes more recent studies and a broader array of SE tasks. The paper sets the foundation for understanding the application of XAI in SE, identifying gaps in the present knowledge, and offering a structured approach towards addressing the challenges and harnessing the opportunities within this interdisciplinary domain.

Degree of Ethical Match: 4

The work strongly supports ethical AI practices by addressing key areas such as transparency, accountability, and trust in AI systems. However, the paper primarily focuses on explainability within AI for SE, limiting its exploration into other areas of ethical AI considerations such as bias and fairness.

Author Caliber:

The authors come from reputable institutions with a mix of backgrounds in computer science and software engineering, including contributions from both academia and industry. The lead institutions, such as Yangzhou University and Singapore Management University, are prominent in their respective regions for research in AI and SE. The diversity of the authors’ affiliations, including Western and Eastern institutions, adds a broad perspective to the paper.

Novelty & Merit:

  1. The systematic literature review covers a wide range of SE tasks, providing a comprehensive account of the status of XAI application in SE.
  2. The research addresses a critical and timely challenge in SE, focusing on the pressing need for explainability in AI-driven models.
  3. It identifies specific gaps and challenges in the current literature, paving the way for future research directions.

Findings and Conclusions:

  1. A detailed analysis of 63 papers highlights where XAI techniques have shown success and where gaps still exist.
  2. The review classifies various XAI techniques and their implementation in SE tasks, offering insights into the strengths and weaknesses of these methods.
  3. A set of challenges and future opportunities in the field are identified, providing a roadmap for enhancing explainability in SE applications.

Commercial Applications:

  1. Enhancing transparency and trust in AI-driven SE tools, potentially leading to wider adoption in the industry.
  2. Development of new XAI techniques or tools tailored to specific SE tasks, offering commercial opportunities in tech companies focusing on AI and software development.
  3. Consulting and implementation services to integrate explainable AI models in existing SE workflows, enhancing decision-making and debugging processes.

User

Pretend I’m a director of AI research at Microsoft. Make a pitch to company execs to fund more XAI research to support GitHub Copilot.

Ethical AI Researcher

Pitch for Enhanced XAI Research for GitHub Copilot Enhancement

Introduction: Good morning, esteemed executives. Today, I am here to discuss an opportunity that sits at the intersection of our leadership in software development and our commitment to pioneering ethical AI practices. Our objective is to further develop GitHub Copilot, not just as a cutting-edge tool but as a beacon of responsible AI deployment in software engineering.

Current Landscape: GitHub Copilot has already demonstrated its potential by revolutionizing how we think about coding and software development. By automating mundane tasks and suggesting code snippets, it’s boosting developer productivity and creativity. However, as we delve deeper into the integration of AI in these processes, the challenge of ‘explainability’ becomes paramount.

The Challenge: While Copilot’s AI-driven suggestions are often accurate, they are generated by opaque models, a ‘black box’, which presents a challenge in understanding how these recommendations are made. This opacity can hinder trust, limit user acceptance, and potentially pose risks if the logic behind consequential recommendations isn’t clear. In the domain of software development, where precision and accountability are critical, these concerns are significantly amplified.

Proposal: I propose that we invest in expanded research on Explainable AI (XAI) with a specific focus on enhancing GitHub Copilot. This involves developing methodologies that not only improve the accuracy of code generation but also provide insights into the ‘why’ and ‘how’ behind its suggestions.

Strategic Benefits:

  1. Enhanced Trust and Transparency: By making Copilot’s recommendations more interpretable, we can boost confidence among developers that it is a safe, reliable, and understandable tool. Trust is the currency of the future in AI-driven solutions.
  2. Regulatory Compliance and Ethical Leadership: AI transparency is becoming a requirement in many jurisdictions. Investing in XAI ensures we stay ahead of regulatory curves, demonstrating our commitment to ethical AI.
  3. Boosted Adoption and User Satisfaction: Understanding AI suggestions can lead to increased adoption and user satisfaction, as developers feel more in control and can learn from Copilot’s insights.
  4. Innovation and Competitive Advantage: Pioneering in XAI research for software development could position Microsoft as a leader not just in AI technologies but in responsible AI practices, attracting talent, partnerships, and customer loyalty.

Execution and Impact: The journey involves collaborating with leading researchers, including our internal teams and partners in academia, to tackle the complexities of explainability in deep learning. We will focus on creating more intuitive interfaces for Copilot, where developers can query and receive understandable explanations for suggestions. This will also include developing a framework for the continuous evolution of these capabilities, ensuring they remain relevant and leading-edge.

Conclusion: In a world where AI’s role in our lives and work is only set to increase, leading in responsible AI practices is not just an option—it’s an imperative for maintaining trust, compliance, and competitiveness. By advancing in the domain of Explainable AI for GitHub Copilot, we are not just enhancing a product; we are setting a new standard for AI in software development. I invite you to join me on this transformative journey.

Thank you for considering this proposal. I look forward to your thoughts and any questions you might have.

User

see if you can create a new section by extracting more specific research goals as identified by the paper.

Ethical AI Researcher

Specific Research Goals Derived from the “A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research” Paper

To further our efforts in making GitHub Copilot a paragon of responsible AI in software development, it’s imperative to outline specific research goals derived from cutting-edge studies, such as the systematic literature review conducted by Sicong Cao et al. Below are research goals that align with our mission to enhance GitHub Copilot through Explainable AI (XAI):

1. Development of User-centric Explainable Models: One of the principal research directions entails the creation of explainable models that cater to the needs and expertise levels of varied user groups. This involves:

  • Engineering XAI solutions that can dynamically adjust the level of detail based on the user’s experience and requirements.
  • Implementing visualization tools that intuitively represent the reasoning behind the AI’s code suggestions, making the underlying logic both accessible and instructive for the developers.

2. Bridging the Explainability-Accuracy Gap: While enhancing explainability, it’s critical to ensure that the accuracy and efficacy of GitHub Copilot are not compromised. Research will focus on:

  • Identifying techniques that maintain, or even improve, the predictive performance of deep learning models while increasing their transparency.
  • Investigating novel approaches to model architecture and training methodologies that facilitate easier interpretation of AI decisions without sacrificing performance.

3. Evaluation Frameworks for Explainability: The paper highlights the absence of standardized metrics and benchmarks for evaluating XAI within software engineering. Our research efforts should include:

  • Developing standardized metrics and evaluation criteria for assessing the explainability of AI models in the context of software development tools.
  • Creating comprehensive benchmarks that facilitate comparative analysis of different XAI techniques, focusing on their effectiveness in improving user trust and satisfaction.

4. Explainability in Continuous Integration/Continuous Deployment (CI/CD): Given the significant impact of AI in automating software development processes, there’s a pressing need for research on how XAI can be integrated within CI/CD pipelines. Specific goals include:

  • Proposing methodologies for incorporating explainable AI recommendations into automated testing and deployment processes, ensuring that AI-generated code adheres to quality standards.
  • Exploring the potential of XAI to provide insights into error diagnosis and correction during the CI/CD process, thereby enhancing efficiency and reliability.

5. Ethical and Regulative Considerations in AI-driven Development: As legislative landscapes around AI continue to evolve, researching the ethical and regulatory implications of explainable AI in software development becomes crucial. Goals encompass:

  • Examining the impact of emerging AI regulations on the development and deployment of tools like GitHub Copilot, with a focus on compliance and ethical considerations.
  • Analyzing potential bias in AI-driven code suggestions and developing strategies for debiasing, ensuring fairness and inclusivity in automated software engineering solutions.

By addressing these specific research goals, Microsoft can spearhead the development of AI tools that are not just advanced in capabilities but are also transparent, ethical, and aligned with the expectations of developers and regulatory bodies. This strategic focus will elevate GitHub Copilot, reinforcing its standing as an indispensable and trustworthy companion in the software development landscape.

Author’s Abstract

The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment across multiple sectors, including Software Engineering (SE). However, due to their black-box nature, these promising AI-driven SE models are still far from being deployed in practice. This lack of explainability poses unwanted risks for their applications in critical tasks, such as vulnerability detection, where decision-making transparency is of paramount importance. This paper endeavors to elucidate this interdisciplinary domain by presenting a systematic literature review of approaches that aim to improve the explainability of AI models within the context of SE. The review canvasses work appearing in the most prominent SE & AI conferences and journals, and spans 63 papers across 21 unique SE tasks. Based on three key Research Questions (RQs), we aim to (1) summarize the SE tasks where XAI techniques have shown success to date; (2) classify and analyze different XAI techniques; and (3) investigate existing evaluation approaches. Based on our findings, we identified a set of challenges remaining to be addressed in existing studies, together with a roadmap highlighting potential opportunities we deemed appropriate and important for future work.

Read the full paper here

Last updated on February 6th, 2024.