Traditional vs. AI-Assisted Source Code Review in IP Litigation: A Comparative Perspective

In recent years, artificial intelligence (AI) has begun reshaping how professionals approach complex technical tasks—from writing code to analyzing it. In the context of intellectual property (IP) litigation, this raises an important question: could AI also support the intricate process of source code review?

Source code review plays a central role in uncovering whether a product infringes software-related patents or misuses protected trade secrets. Traditionally, this work has been led by technical experts conducting deep manual reviews of a product’s internal code. However, as AI tools become increasingly capable of summarizing logic and identifying patterns, legal teams are beginning to explore whether these technologies could complement human expertise.

This blog examines the current landscape of traditional and AI-assisted source code review, offering a comparative view of their benefits, limitations, and what the future may hold for IP litigation workflows.

The Importance of Source Code in IP Disputes

Source code review has become an indispensable part of high-technology litigation. Many key features of patent claims—such as background processes, server-side interactions, or low-level data handling—are impossible to detect through product testing alone. In these cases, the only way to determine whether a product infringes a patent is by analyzing its internal code.

Source code contains the DNA of a software product: how it stores data, how it interacts with other components, and how it executes specific operations. Reviewing this code allows litigation teams to trace control flows, evaluate variable usage, identify key function calls, and understand exactly how a product behaves under the hood. These insights help build expert reports, shape infringement contentions, and drive case strategy.

In fact, software, networking, and communication technologies now account for approximately 70% of all U.S. patent litigations, reflecting the critical role of technical evidence like source code in enforcement efforts.[1] 

Traditional Source Code Review: Human-Led and Court-Tested

In most IP litigation settings, source code review is performed manually by experienced technical consultants. Using tools like PowerGREP, VSCode, and Notepad++, reviewers can sift through thousands of files to find evidence that maps to asserted claims.

This process is deliberately meticulous. Analysts follow logical breadcrumbs, interpret code behavior, and translate their findings into legally digestible insights. The strength of this method lies in its accuracy, interpretability, and defensibility, since human reviewers can account for context, resolve ambiguity, and provide expert testimony grounded in their direct observations.

However, traditional review is also resource intensive. Manually combing through a sprawling codebase can take weeks or months, especially when multiple patents and accused products are involved. Scalability in particular becomes a concern, especially under tight deadlines.

AI-Assisted Review

In recent years, artificial intelligence has made waves across the software development lifecycle. AI-powered tools can now generate code, refactor existing code, and summarize logic using large language models (LLMs). Some LLMs can even identify repetitive logic patterns or predict where bugs are likely to occur.

In theory, these capabilities could be applied to litigation-focused code review as well. For instance:

• LLMs could help summarize long functions or unfamiliar syntax
• Pattern recognition tools could flag similar implementations across files
• AI search could help triage massive codebases before a deep dive

Several academic studies—such as Qodo PR Agent’s deployment in pull request workflows[2] —have shown promise in boosting review speed and identifying subtle issues.

Industry surveys also suggest that AI-assisted tools are gaining rapid traction. For example, usage of AI-powered review tools among developers jumped from 39% to over 75% in just five months, with platforms like Github Copilot Reviewer, Cursor Bugbot, and CodeRabbit leading the charge.[3]

Adoption Barriers and Industry Realities

While AI-assisted tools have made major strides in software development, their use in IP litigation source code review is still emerging. The core challenge isn’t interest, but rather infrastructure.

Most reviews are conducted in tightly controlled environments provided by opposing counsel, which are designed to protect data confidentiality and preserve evidentiary integrity. These machines often:

• Restrict internet access
• Limit the installation of third-party tools
• Disallow exporting code or screenshots

Because of these constraints, integrating modern AI tools directly into live review workflows remains a work in progress, and many existing solutions have not been built with offline or court-compliant use in mind.

That being said, the growing capabilities of AI have sparked active conversations around secure deployment models. There is clear potential for AI tools to support source code reviewers—whether in pre-review preparation, pattern triage, or post-review documentation—especially if implemented with transparency and validation safeguards in place.

The Future: Hybrid Workflows and Human Oversight

Looking ahead, the future of source code review in litigation is likely to be collaborative. Rather than replacing human reviewers, AI tools are positioned to amplify their efficiency—especially as more secure, explainable, and offline-ready models emerge.

The legal community is already exploring pilot workflows, where AI supports tasks like:

• Pre-review code summarization
• Repeated pattern detection across large codebases
• Generating initial mappings for human refinement

According to the ILTA 2024 Technology Survey, nearly two-thirds of legal professionals now believe generative AI will be used for summarizing complex documents, up from less than half just a year ago.[4] This marks a growing recognition of AI’s value in litigation tasks. Additionally, a recent report by Ari Kaplan Advisors found that 87% of litigation support directors consider AI-assisted case management tools a competitive advantage, especially in light of increasing case volumes and expanding data complexity.[4]

While source code review presents its own unique challenges, this broader industry shift reflects increasing openness to AI-supported workflows across legal practice. With proper validation protocols and clear audit trails, these tools can reduce time-to-insight without compromising legal defensibility. The key will be to ensure that human expertise remains at the center, with AI serving as a force multiplier rather than a black box.

Source code review remains a cornerstone of high-technology litigation, and as AI tools continue to evolve, so too does the opportunity to bring greater efficiency and insight into this critical process. At UnitedLex, we’re actively monitoring developments in AI-assisted review while maintaining the rigorous standards that IP litigation demands. By thoughtfully combining innovation with our deep technical expertise, we help clients stay ahead—confidently and securely.