Priya Sharma

General 7 min

Priya Sharma • 41 minutes ago

Inside UT Austin's Refactoring-Resistant Code Similarity Pipeline

When the University of Texas at Austin’s CS 312 course saw a spike in suspicious submissions that evaded their existing checks, they turned to token-based similarity analysis to catch code that had been renamed, reorganized, and logic-swapped. This case study walks through the techniques, the results, and the lessons for any institution facing refactoring-resistant plagiarism.

Read

General 10 min

Priya Sharma • 1 day ago

The Long Road to Refactoring-Resistant Code Plagiarism Detection

Code refactoring — renaming variables, reordering statements, extracting functions — has long been the easiest way for students to disguise copied code. This article traces the thirty-year arms race between obfuscation tactics and detection techniques, from simple string comparison to modern AST and graph-based analysis that can spot similarities even after heavy transformation. Understanding this history explains why no single method is perfect and how layered approaches like Codequiry’s hybrid engine achieve the highest accuracy.

Read

AI Detection 9 min

Priya Sharma • 6 days ago

How Burstiness and Perplexity Catch AI-Generated Code

Burstiness and perplexity aren't just linguistic curiosities—they're the primary statistical signals that distinguish human-written source code from LLM output. This article explains exactly how these measures work under the hood, with worked examples, real-world detection rates, and honest limitations.

Read

General 9 min

Priya Sharma • 1 week ago

The Measurable Impact of Static Analysis on Student Code Quality

A semester-long controlled experiment across two sections of an introductory programming course shows that students who receive automated static analysis feedback produce measurably cleaner, more maintainable code. Cyclomatic complexity dropped 22%, test coverage rose 29%, and common code smells decreased by 38%. Here’s the methodology, the data, and what it means for code-scanning in education.

Read

Academic Integrity 10 min

Priya Sharma • 1 week ago

Contextualizing Programming Problems to Reduce Cheating

Instead of fighting plagiarism after submissions arrive, you can design assignments that are inherently resistant to copying. By embedding unique, student-specific context into problem statements, you make it obvious when code has been copied and also harder for AI tools to produce a correct answer. This article covers concrete techniques—parameterized test cases, local data imports, and narrative hooks—that real universities have used to cut similarity rates by over 40%.

Read

General 13 min

Priya Sharma • 2 months ago

How a TA Spots Refactored Code in 300 Java Submissions

Teaching assistants often face the challenge of detecting code plagiarism when students refactor submissions to evade similarity checkers. This article profiles one TA's workflow using AST-based analysis and structural fingerprinting to catch plagiarized code in a large introductory Java course, with practical techniques applicable to any programming educator.

Read

Tutorials 11 min

Priya Sharma • 2 months ago

A Checklist for Integrating Code Scanning Into Your CI Pipeline

Manual code review alone can't catch every bug or security vulnerability. This practical guide walks you through building a robust code scanning pipeline that integrates directly into your CI/CD workflow, covering static analysis, dependency scanning, secret detection, and policy enforcement with concrete tool configurations and real-world examples.

Read

AI Detection 8 min

Priya Sharma • 2 months ago

Your AI Detection Tool Is Probably a Random Number Generator

The market is flooded with tools claiming to spot AI-written code with 99% accuracy. Most are built on statistical sand. We dissect the eight fundamental flaws, from dataset contamination to meaningless confidence scores, that render their outputs little better than a coin flip for serious applications.

Read

General 6 min

Priya Sharma • 2 months ago

Your Static Analysis Tool Is Lying to You About Complexity

We've been sold a bill of goods on code complexity. The metrics your static analysis tool proudly reports—cyclomatic complexity, lines of code, nesting depth—are not just incomplete; they're actively misleading. They create a false sense of security while the real architectural debt compounds silently. It's time to measure what actually matters.

Read

General 8 min

Priya Sharma • 2 months ago

The Open Source Audit That Nearly Bankrupted a Startup

When a promising fintech startup sought Series B funding, their technical due diligence triggered a nightmare. A deep code audit revealed a sprawling, undocumented web of open-source license violations, putting their entire intellectual property—and survival—at risk. This is the story of how they navigated the legal and technical fallout, and why your codebase might be hiding the same ticking bomb.

Read

General 8 min

Priya Sharma • 2 months ago

The Open Source Library That Almost Got a Startup Sued

When a fintech startup's MVP launched, they received a cease-and-desist letter from a major software consortium. The culprit wasn't stolen IP—it was a 15-line function copied from a Stack Overflow answer, carrying a viral open-source license. This is the story of how hidden license contamination almost sank a company before Series A.

Read

General 8 min

Priya Sharma • 3 months ago

The Assignment That Broke Every Plagiarism Checker

When a Stanford CS106A professor noticed identical, bizarre logic errors across dozens of student submissions, she uncovered a cheating method no standard tool could catch. This is the story of how students exploited the very algorithms designed to stop them, and what it revealed about the blind spots in automated code similarity detection. The fallout changed how the department thinks about academic integrity.

Read