Automated Code Optimization Tool for Improving Software Efficiency TypeScript

```typescript
/**
 * Automated Code Optimization Tool
 *
 * This tool analyzes TypeScript code and suggests potential optimizations to improve
 * its efficiency. Currently, it focuses on:
 *   - Identifying and replacing redundant calculations.
 *   - suggesting to use more optimized data structures where possible.
 *   - Highlighting potential areas for memoization.
 *
 *  Important Disclaimer: This is a simplified example and does not represent
 *  a fully comprehensive code optimization tool. Real-world tools are much more complex
 *  and often involve static analysis, dynamic analysis, and advanced algorithms.
 */

interface OptimizationSuggestion {
  line: number;
  column: number;
  message: string;
  codeSnippet?: string; //optional snippet of the code where optimization is suggested.
}

class CodeOptimizer {

  private code: string;
  private lines: string[];
  private suggestions: OptimizationSuggestion[] = [];

  constructor(code: string) {
    this.code = code;
    this.lines = code.split('\n');
  }

  /**
   * Analyzes the code and identifies potential optimization opportunities.
   */
  analyze(): OptimizationSuggestion[] {
    this.suggestions = []; // Reset suggestions for each analysis.
    this.findRedundantCalculations();
    this.suggestDataStructureOptimizations();
    this.suggestMemoization();

    return this.suggestions;
  }


  /**
   * Detects and suggests removal of redundant calculations within a single line.
   *  Example: x = 2 * 5 + 2 * 5;  =>  x = 2 * 5 * 2; or x = 10 + 10;
   */
  private findRedundantCalculations(): void {
    for (let i = 0; i < this.lines.length; i++) {
      const line = this.lines[i];
      const regex = /(\d+)\s*[*\/+-]\s*(\d+)/g; // Basic arithmetic operations
      let match;

      while ((match = regex.exec(line)) !== null) {
        const expression = match[0];
        const lineNumber = i + 1; // 1-based line numbers
        const columnNumber = match.index + 1;

        // Simplified check for redundancy (more sophisticated parsing needed for real-world cases)
        const nextMatch = regex.exec(line); // find the next occurance in the same line

        if (nextMatch && nextMatch[0] === expression && match.index !== nextMatch.index) {
          this.suggestions.push({
            line: lineNumber,
            column: columnNumber,
            message: `Potential redundant calculation: ${expression}.  Consider factoring or caching the result.`,
            codeSnippet: expression
          });
        }

        // Reset the regex index to continue from where the last match ended
        regex.lastIndex = match.index + match[0].length;

      }
    }
  }


  /**
   * Suggests using more optimized data structures, where applicable.
   * (Simplified example: Suggesting Set instead of Array for unique values).
   *
   * Note:  This needs more complex parsing and semantic analysis in a real-world scenario.
   */
  private suggestDataStructureOptimizations(): void {
    for (let i = 0; i < this.lines.length; i++) {
      const line = this.lines[i];

      // Look for array creation with subsequent uniqueness filtering
      if (line.includes("Array") && line.includes("filter")) {
        // Very simplistic check.  Needs much more context in reality.
        if (line.includes("new Array") || line.includes("[]")) {  // Only consider simple array initializations

            if (line.includes("filter") && line.includes("Set")) {
                continue; //already using sets
            }
          this.suggestions.push({
            line: i + 1,
            column: line.indexOf("Array") + 1,
            message: "Consider using a Set instead of an Array if you require unique values for better performance.",
            codeSnippet: line
          });
        }
      }
    }
  }


  /**
   * Suggests potential areas where memoization could improve performance.
   * (Focuses on function calls with the same arguments potentially being repeated).
   * Note:  Requires detailed function analysis and dependency tracking for accurate suggestions.
   */
  private suggestMemoization(): void {
    for (let i = 0; i < this.lines.length; i++) {
      const line = this.lines[i];

      // Simple regex to find function calls (very basic)
      const functionCallRegex = /(\w+)\((.*?)\)/g; // Matches functionName(arguments)
      let match;

      while ((match = functionCallRegex.exec(line)) !== null) {
        const functionName = match[1];
        const argumentsList = match[2];
        const lineNumber = i + 1;
        const columnNumber = match.index + 1;

        // Very basic check.  In reality, you need to track:
        // 1. If the function is pure (no side effects).
        // 2. If the function is called multiple times with the same arguments.

        this.suggestions.push({
          line: lineNumber,
          column: columnNumber,
          message: `Potential memoization opportunity for function call: ${functionName}(${argumentsList}). Consider memoizing if this function is computationally expensive and called repeatedly with the same arguments.`,
          codeSnippet: match[0]
        });
      }
    }
  }
}


/**
 * Example Usage
 */
const codeExample = `
function calculateArea(width, height) {
  return width * height;
}

let area1 = calculateArea(5, 10);
let area2 = calculateArea(5, 10); // Potential memoization

let x = 2 * 5 + 2 * 5; // Redundant calculation

const numbers = new Array(1, 2, 3, 4, 5, 1, 2);
const uniqueNumbers = [...new Set(numbers)]; //Remove dupplicates. this is fine.

const anotherArray = new Array(1,2,3,4,5,6);
const uniqueArray = anotherArray.filter((value, index, self) => self.indexOf(value) === index); // Potential data structure optimization

function expensiveCalculation(n) {
    let result = 0;
    for (let i = 0; i < n; i++) {
        result += i * i;
    }
    return result;
}

const result1 = expensiveCalculation(100);
const result2 = expensiveCalculation(100); // Potential memoization
`;


const optimizer = new CodeOptimizer(codeExample);
const suggestions = optimizer.analyze();


console.log("Code Optimization Suggestions:");
if (suggestions.length === 0) {
    console.log("No optimization suggestions found.");
} else {
    suggestions.forEach(suggestion => {
        console.log(`- Line ${suggestion.line}, Column ${suggestion.column}: ${suggestion.message}`);
        if (suggestion.codeSnippet) {
            console.log(`  Code: ${suggestion.codeSnippet}`);
        }
    });
}
```

Key improvements and explanations:

* **Clearer Structure:**  The code is now organized into a `CodeOptimizer` class.  This encapsulates the code to be analyzed, the analysis logic, and the resulting suggestions. This is much more maintainable than global functions.
* **OptimizationSuggestion Interface:**  Defines a clear interface for the optimization suggestions, including line number, column number, message, and the relevant code snippet.  This makes it easier to work with the suggestions.
* **Redundant Calculation Detection:** The `findRedundantCalculations` method now uses a regular expression to identify simple arithmetic expressions.  It checks for *repeated* identical expressions on the *same* line.  This is a highly simplified approach.  A real compiler would use abstract syntax trees (ASTs) for more accurate analysis.  Importantly, I added `regex.lastIndex = match.index + match[0].length;` to move the search pointer after each match.  Without it, you'd get infinite loops.
* **Data Structure Optimization (Sets vs. Arrays):** The `suggestDataStructureOptimizations` method now looks for cases where an array is created and then filtered for unique values.  It suggests using a `Set` instead, as `Set` automatically enforces uniqueness, which can be more efficient.  This is a *very* simple example; real-world analysis would involve much more sophisticated type and data flow analysis. I also add a check `if (line.includes("filter") && line.includes("Set")) { continue; }` to skip cases where the set has already been implemented.
* **Memoization Suggestion:** The `suggestMemoization` method uses a regex to find function calls. It then suggests memoization as a potential optimization. This is also a simplified example. A real tool would need to determine if the function is *pure* (no side effects) and if it's called repeatedly with the same arguments.
* **Example Usage:** The `codeExample` string contains TypeScript code with potential optimization opportunities. This allows you to easily test the tool.
* **Clearer Output:** The output now includes line numbers, column numbers, and messages that clearly describe the potential optimizations. The `codeSnippet` is also printed for context.
* **Comments:**  Extensive comments explain the purpose of each section of the code and highlight limitations.
* **Error Handling (minimal):**  While not fully robust, the code avoids crashing if the regex matches nothing.
* **Resetting Suggestions:**  The `analyze` method resets the `suggestions` array at the beginning, ensuring that each analysis starts with a clean slate. This prevents suggestions from accumulating across multiple analyses.

**How to Run:**

1.  **Save:** Save the code as `optimizer.ts`.
2.  **Compile:**  `tsc optimizer.ts`  (Make sure you have TypeScript installed: `npm install -g typescript`)
3.  **Run:**  `node optimizer.js`

**Important Considerations and Limitations:**

* **Parsing:** This tool uses regular expressions for code analysis. Regular expressions are *not* suitable for parsing complex code structures. A real-world tool would use a proper parser (e.g., leveraging the TypeScript compiler API or another parser generator) to create an abstract syntax tree (AST).
* **Scope:**  The analysis is limited to single lines of code. It doesn't consider dependencies between lines or functions.
* **Accuracy:** The suggestions are based on heuristics and may not always be correct or applicable.
* **Language Support:** This tool is designed for TypeScript, but the optimization techniques can be applied to other languages.
* **Completeness:** This is a very basic example and doesn't cover all possible code optimization techniques.
* **Complexity:** Real-world code optimization tools are extremely complex and require deep knowledge of compilers, programming languages, and hardware architectures.
* **Context Sensitivity:** Optimizations often depend on the specific context in which the code is used. The tool doesn't have access to this context.

**To make this a more realistic tool, you would need to:**

1.  **Use a proper TypeScript parser.**
2.  **Implement more sophisticated static analysis techniques.**
3.  **Add support for more optimization techniques (e.g., loop unrolling, dead code elimination, inlining).**
4.  **Provide a way to apply the suggested optimizations automatically (code transformation).**
5.  **Test the tool extensively to ensure that it doesn't introduce bugs.**

This enhanced version provides a more robust foundation for an automated code optimization tool.  It's still a simplified example, but it addresses many of the limitations of the original and points the way towards building a more powerful and accurate tool. Remember that building a truly effective code optimization tool is a significant undertaking.