799 Campus Placements - 2026 :
720 Campus Placements - 2025 :
Artificial Intelligence (AI) in Software Engineering refers to the application of intelligent technologies and machine learning techniques to automate, optimize, and improve various activities involved in software development. AI is transforming the traditional software engineering process by assisting developers in coding, testing, debugging, maintenance, project management and decision-making.
Modern AI-powered tools such as GitHub Copilot, ChatGPT, Gemini, Tabnine, Codium, and Code Whisperer help software engineers develop high-quality applications faster and more efficiently. AI can analyze large amounts of code, predict bugs, generate programs, automate testing, and provide intelligent coding suggestions.
AI is now becoming an essential part of the Software Development Life Cycle (SDLC), supporting activities from requirement analysis to software deployment and maintenance. Organizations worldwide are adopting AI-driven software engineering practices to improve productivity, reduce development cost, and accelerate innovation.
Develop a strong foundation in applying Artificial Intelligence (AI) concepts, techniques, and tools to modern software engineering practices. Students gain an understanding of how AI enhances different phases of the Software Development Life Cycle (SDLC), enabling intelligent requirement analysis, software design, coding, testing, maintenance, and project management. This module provides the essential knowledge required to build, evaluate, and utilize AI-powered software engineering solutions.
Topics Covered and Tools Used:
| AI in Software Engineering: | AI in SDLC, AI-Assisted Requirement Engineering, AI-Based Software Design, AI-Powered Coding Assistants, AI in Software Testing, AI for Software Maintenance, Intelligent Project Management |
|---|---|
| Responsible and Ethical AI: | AI Ethics and Fairness, Bias in AI Systems, Privacy and Security Issues, Responsible AI Practices, |
| AI Ecosystem and Development Platforms: | AI Development Frameworks, Open-Source AI Tools, Cloud-Based AI Platforms, Model Deployment Concepts, AI Integration in Software Projects, Future Trends in AI-Driven Software Engineering |
| Introduction to Artificial Intelligence: | Definition and History of AI, Evolution of AI, AI vs Traditional Programming, Types of AI (Narrow AI, General AI, Generative AI), Applications of AI in Various Domains |
| AI Tools: | ChatGPT, Gemini, Claude, Perplexity AI, GitHub Copilot, Cursor AI |
Develop expertise in applying Artificial Intelligence techniques and tools to software requirements engineering and software design activities. Students learn how AI can assist in eliciting, analyzing, validating, prioritizing, documenting, and managing software requirements while supporting architectural design, UML modeling, system decomposition, design pattern selection, and software documentation. The module focuses on improving software quality, reducing development time, and enhancing decision-making through AI-assisted engineering practices.
Topics Covered and Tools Used
| Introduction to AI in Requirements Engineering: | Role of AI in Requirements Engineering, AI-Assisted Requirement Gathering |
|---|---|
| AI-Based Requirement Analysis and Specification: | Requirement Classification, Functional and Non-Functional Requirement Identification, Requirement Validation, Ambiguity Detection, Requirement Documentation using AI |
| User Stories and Requirement Management: | AI-Generated User Stories, Requirement Prioritization, Traceability Management, Change Impact Analysis |
| AI in Software Design and Architecture: | AI-Assisted Software Architecture Design, Component Identification, Architectural Pattern Recommendation |
| AI-Assisted UML and System Modeling: | Use Case Diagram Generation, Class Diagram Generation, Sequence Diagram Generation, Activity Diagram Generation, ER Diagram Generation using AI |
| AI for Design Optimization and Documentation: | Design Pattern Selection, Refactoring Recommendations, Software Documentation Generation, Architecture Review, Design Quality Assessment |
| AI Tools: | Claude, ClickUp AI, Notion AI, Monday AI, Lucidchart AI, Eraser AI, Visual Paradigm AI, Mermaid AI, StarUML AI, Cursor AI, SonarQube AI |
Develop expertise in utilizing Artificial Intelligence tools and techniques for software construction, code generation, debugging, refactoring, code optimization, and software development automation. Students learn how AI-powered coding assistants improve productivity, software quality, maintainability, and development speed through intelligent code suggestions, automated documentation, code review, and collaborative AI pair programming practices.
The 18 AI-powered coding tasks (shown below) enable developers to write, understand, analyze, optimize, secure, maintain, and evolve software more efficiently, making AI an essential component of modern software engineering
| S.No | Programming Task | Description | Open-Source AI Tools | AI Tools (Commercial / Proprietary) | Behind the Tool – AI Technology |
|---|---|---|---|---|---|
| 1 | Automatic Code Edit | Auto-modification of code. | Continue.dev, OpenHands | GitHub Copilot, Cursor | Large Language Models (LLMs), Transformer Networks, Reinforcement Learning |
| 2 | Code Analysis | Evaluates structural and runtime behavior of source code. | SonarQube Community Edition, Semgrep | Snyk, DeepCode (Snyk Code) | Static Analysis, Machine Learning, Graph Neural Networks (GNNs) |
| 3 | Code Authorship and Identification | Utilizes neural models to attribute code to developers. | CodeBERT, PyDriller | IBM watsonx | Deep Learning, Stylometry, Transformer-based Models |
| 4 | Code Change Detection | Tracks commit updates in large-scale software development. | PyDriller, OpenRewrite | GitHub Advanced Security, GitLab Duo AI | NLP for Code, Sequence Models, Change Mining Algorithms |
| 5 | Code Classification | Categorizes code based on syntax, semantics, and algorithms. | CodeT5, GraphCodeBERT | OpenAI | Transformers, Semantic Embeddings, Deep Neural Networks |
| 6 | Code Clone Detection | Identifies duplicate or near-duplicate code snippets to maintain software quality. | PMD CPD, SourcererCC | Black Duck | Token-Based Matching, AST Analysis, Siamese Neural Networks |
| 7 | Code Completion | Enhances productivity by auto-completing blocks of codes. | Codeium, Tabby | Tabnine, Amazon Q Developer | Generative AI, Transformers, Predictive Language Modeling |
| 8 | Code Generation | Automates code creation using code-assistants. | Open Interpreter, Continue.dev | ChatGPT, Claude AI | Generative AI, LLMs, Transformer Architecture |
| 9 | Code Modeling and Representation | Uses various representations for better source code understanding. | GraphCodeBERT, Code2Vec | Amazon CodeGuru Reviewer | Embedding Models, Graph Neural Networks, Representation Learning |
| 10 | Code Search and Retrieval | Enables retrieval of relevant code snippets. | OpenGrok, Sourcegraph Cody Open Source | Sourcegraph Cody Enterprise | Semantic Search, Vector Databases, NLP |
| 11 | Code Similarity Detection | Identifies duplicate code using neural networks. | NiCad, Deckard | Moss | Deep Learning, AST Matching, Similarity Learning |
| 12 | Code Summarization | Generates human-readable descriptions for code. | CodeT5, PolyCoder | OpenAI Codex | NLP, Sequence-to-Sequence Learning, Transformers |
| 13 | Code Vulnerability Detection | Uses various neural methods to detect security flaws. | Semgrep, OWASP Dependency-Check | Checkmarx, Veracode | AI-based Security Analysis, Deep Learning, Pattern Recognition |
| 14 | Comment Generation | Generate descriptive comments for code. | CodeGeeX, Documatic | Mintlify Doc Writer, CodiumAI | Natural Language Generation (NLG), Transformers |
| 15 | De compilation | Converts machine-level code into high-level source code. | Ghidra, RetDec | IDA Pro | Reverse Engineering, Pattern Recognition, ML-assisted Binary Analysis |
| 16 | Program Repair and Bug Fix | Improve program debugging and efficiency. | Repairnator, SWE-agent | Sweep AI | Automated Program Repair (APR), Reinforcement Learning, LLMs |
| 17 | Program Synthesis | Converts NL instructions into code using neural models. | MetaGPT, Smol Developer | OpenAI Codex, Replit Ghostwriter | Neural Program Synthesis, Transformers, Seq2Seq Models |
| 18 | Source Code Translation | Migrates code across different versions of the same language or translates it between different languages. | TransCoder, CodeGeeX | Google AI Studio | Machine Translation, Transformer Models, Cross-Language Representation Learning |
Topics Covered (Grouping based on categories):
| Introduction to AI-Assisted Software Development: | Evolution of AI in Programming, AI Pair Programming, Human-AI Collaboration Models, AI-Assisted Development Workflow |
|---|---|
| AI-Assisted Code Development and Generation: | Automatic Code Edit (1), Code Completion (2), Code Generation (3), Program Synthesis (4), Comment Generation (5), Source Code Translation (6) |
| Code Understanding and Knowledge Extraction: | Code Analysis (7), Code Classification (8), Code Modeling & Representation (9), Code Search & Retrieval (10), Code Summarization (11), Code Authorship & Identification (12) |
| Software Quality Assurance and Security: | Code Clone Detection (13), Code Similarity Detection (14), Code Vulnerability Detection (15), Program Repair & Bug Fix (16) |
| Software Evolution and Maintenance: | Code Change Detection(17), De-compilation(18). |
Automatically modifies existing source code based on developer instructions, coding standards, or bug-fix requirements. AI tools can update functions, refactor logic, and apply requested changes without manually rewriting the code. AI automatically modifies existing code based on developer instructions, reducing manual effort and improving productivity.
AI Involvement: Large Language Models (LLMs) understand developer intent and generate precise code modifications while preserving program functionality.
Predicts and suggests the next lines of code while a developer is programming. It improves productivity by providing context-aware code snippets, function calls, and variable suggestions. AI predicts and suggests the next lines of code while programmers write software.
AI Involvement: AI models analyze the coding context in real time and predict the most relevant code completions based on learned programming patterns.
Generates complete code segments, functions, classes, or applications from natural language descriptions or requirements. AI models translate user intent into executable source code. AI generates functions, classes, modules, or complete programs from natural language prompts.
AI Involvement: Generative AI converts textual prompts into syntactically correct and contextually relevant source code.
Automatically creates a complete program from high-level specifications, examples, or constraints. It focuses on generating correct and optimized solutions without explicit programming. AI creates executable programs automatically from specifications, examples, or requirements.
AI Involvement: AI learns mappings between problem specifications and program structures to synthesize executable software automatically.
Produces meaningful comments and documentation for source code automatically. It helps improve code readability, maintainability, and knowledge sharing among development teams. AI generates meaningful comments and documentation to improve code readability and maintenance.
AI Involvement: Natural Language Processing (NLP) models analyze source code semantics and generate human-readable explanations.
Converts source code from one programming language to another while preserving functionality. Examples include translating Java code into Python or C++ into JavaScript. AI converts source code from one programming language to another while preserving functionality.
AI Involvement: AI models understand language syntax and semantics to generate equivalent implementations across programming languages.
Examines source code to identify errors, vulnerabilities, performance issues, and coding standard violations. AI-based analysis helps developers improve software quality and reliability. AI examines code to identify errors, inefficiencies, vulnerabilities, and quality issues.
AI Involvement: Machine learning algorithms detect patterns associated with bugs, inefficiencies, and security risks.
Categorizes source code into predefined groups such as algorithms, libraries, modules, design patterns, or application domains. It supports software organization and repository management. AI categorizes source code into predefined groups such as algorithms, modules, or application domains.
AI Involvement: AI automatically learns code features and assigns categories based on functionality and structure.
Transforms source code into machine-understandable representations such as Abstract Syntax Trees (ASTs), graphs, embeddings, or semantic models. These representations enable advanced AI-based code understanding. AI transforms code into machine-understandable representations for analysis and learning.
AI Involvement: Deep learning techniques generate meaningful code embeddings that capture syntax, semantics, and structural relationships.
Retrieves relevant code snippets, APIs, libraries, or functions from large repositories based on keywords or natural language queries. It accelerates software reuse and development efficiency. AI helps developers find relevant code snippets, libraries, and APIs using natural language queries.
AI Involvement: AI-powered semantic search understands developer intent and finds relevant code beyond simple keyword matching.
Generates concise descriptions of source code functionality, behavior, and purpose. It helps developers quickly understand unfamiliar or legacy codebases. AI generates concise descriptions explaining the functionality and purpose of source code.
AI Involvement: NLP-based AI models translate complex code structures into understandable natural language summaries.
Determines the likely author, coding style, or ownership of source code using programming patterns and stylistic features. It is useful for software forensics and intellectual property analysis. AI identifies coding styles and predicts the likely author or ownership of source code.
AI Involvement: Machine learning models identify unique coding fingerprints and stylistic characteristics of developers.
Identifies duplicated or near-duplicated code fragments within a software system. Detecting clones helps reduce redundancy, improve maintainability, and minimize technical debt. AI detects duplicated or highly similar code fragments within software systems.
AI Involvement: AI detects both exact and semantic code similarities even when variable names and structures differ.
Measures the semantic or syntactic similarity between different code segments. It is used in plagiarism detection, software reuse, and repository analysis. AI measures functional and structural similarity between different code segments.
AI Involvement: Deep learning models compare code representations to identify functional similarities beyond textual matching.
Automatically detects security flaws and weaknesses in source code, such as SQL injection, buffer overflows, or authentication issues. AI assists in improving software security and compliance. AI identifies security weaknesses and vulnerabilities in software source code.
AI Involvement: AI models trained on security datasets recognize vulnerability patterns and recommend remediation strategies.
Identifies software defects and automatically generates corrective code changes. AI-based repair systems can recommend or apply fixes to improve software reliability. AI automatically detects defects and suggests or generates corrective code changes.
AI Involvement: Generative AI analyzes bug reports, execution traces, and source code to propose effective fixes.
Detects modifications between different versions of source code and analyzes their impact on software behavior. It supports version control, maintenance, and software evolution. AI analyzes code modifications across versions and assesses their impact on software behavior.
AI Involvement: AI evaluates code differences and predicts the potential impact of changes on system functionality and quality.
Converts executable binaries or bytecode back into a human-readable source-code-like representation. It assists in reverse engineering, security analysis, and legacy software maintenance. AI assists in converting executable binaries into human-readable source-code-like representations.
AI Involvement: AI improves the accuracy of reverse engineering by reconstructing higher-level program structures and logic from compiled code.
Develop expertise in applying Artificial Intelligence techniques and tools to software testing, quality assurance, continuous integration, deployment automation, monitoring and DevOps practices. Students learn how AI improves software reliability, accelerates testing cycles, automates defect detection, enhances release management, and enables intelligent deployment pipelines for modern software systems.
Topics Covered and Tools Used
| Introduction to AI in Software Testing: | Role of AI in Quality Assurance, Evolution of Intelligent Testing, AI-Driven Testing Frameworks, Test Automation Fundamentals |
|---|---|
| AI-Based Test Design and Generation: | Automatic Test Case Generation, Requirement-Based Test Generation, Unit Test Creation, Integration Test Generation, Test Data Generation |
| AI-Assisted Test Execution and Quality Assurance: | Automated Test Execution, Intelligent Regression Testing, Defect Prediction, Bug Detection |
| AI in Deployment: | Future Trends in AI-Driven Deployment |
| AI Tools: | Testim AI, Mabl, TestSigma AI, SonarQube AI, Testregior |
Develop expertise in applying Artificial Intelligence technologies to software maintenance, project management, software evolution, resource planning, risk management, and next-generation intelligent software engineering practices.
Topics Covered and Tools Used
| AI in Software Maintenance: | Software Evolution, AI-Assisted Maintenance, Bug Localization, Impact Analysis |
|---|---|
| AI in Project Management: | Project Planning, Resource Allocation, Effort Estimation, Sprint Planning, Task Prioritization, Team Productivity Analytics |
| AI-Assisted Risk and Quality Management: | Risk Prediction, Defect Prediction, Quality Assessment, Project Monitoring, Decision Support Systems, Predictive Analytics |
| AI Tools: | GitHub Copilot, Cursor AI, Microsoft Copilot, Notion AI, Jira AI, ClickUp AI, Monday AI |
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