Benchmark: CS Degrees vs. Bootcamps 2026: Job Placement Rates for Python 3.14 and Java 23 Developers

# placement_analyzer.py
# Python 3.14.0-rc2
# Analyzes CS degree vs bootcamp placement rates for Python 3.14/Java 23 roles
# Requires: pandas>=2.2.0, numpy>=1.26.0

import csv
import sys
from dataclasses import dataclass
from typing import Dict, List, Optional
from enum import Enum

class EducationType(Enum):
    CS_DEGREE = \"cs_degree\"
    BOOTCAMP = \"bootcamp\"

class Language(Enum):
    PYTHON_314 = \"python_3.14\"
    JAVA_23 = \"java_23\"

@dataclass
class CandidateRecord:
    \\\"\\\"\\\"Stores anonymized placement data for a single candidate\\\"\\\"\\\"
    candidate_id: str
    education: EducationType
    primary_language: Language
    months_to_placement: Optional[int]  # None if not placed in 6 months
    first_year_tc: Optional[int]  # None if not placed

def load_placement_data(filepath: str) -> List[CandidateRecord]:
    \\\"\\\"\\\"Loads placement data from a CSV file, handles malformed rows\\\"\\\"\\\"
    records = []
    required_headers = {\"candidate_id\", \"education\", \"primary_language\", 
                        \"months_to_placement\", \"first_year_tc\"}

    try:
        with open(filepath, 'r', encoding='utf-8') as f:
            reader = csv.DictReader(f)
            # Validate headers
            if not required_headers.issubset(reader.fieldnames):
                missing = required_headers - set(reader.fieldnames)
                raise ValueError(f\"Missing required headers: {missing}\")

            for row_num, row in enumerate(reader, start=2):  # row 1 is header
                try:
                    # Parse education type
                    edu = EducationType(row[\"education\"].lower())
                    # Parse primary language
                    lang = Language(row[\"primary_language\"].lower())
                    # Parse months to placement (None if empty or >6)
                    mtp = row[\"months_to_placement\"].strip()
                    months = None if not mtp or int(mtp) > 6 else int(mtp)
                    # Parse first year TC (None if not placed)
                    tc = row[\"first_year_tc\"].strip()
                    first_year_tc = None if not tc else int(tc)

                    records.append(CandidateRecord(
                        candidate_id=row[\"candidate_id\"],
                        education=edu,
                        primary_language=lang,
                        months_to_placement=months,
                        first_year_tc=first_year_tc
                    ))
                except (ValueError, KeyError) as e:
                    print(f\"Skipping malformed row {row_num}: {e}\", file=sys.stderr)
                    continue
    except FileNotFoundError:
        print(f\"Error: File {filepath} not found.\", file=sys.stderr)
        sys.exit(1)
    except Exception as e:
        print(f\"Unexpected error loading data: {e}\", file=sys.stderr)
        sys.exit(1)

    return records

def calculate_placement_rate(records: List[CandidateRecord], 
                            edu_type: EducationType,
                            lang: Optional[Language] = None) -> float:
    \\\"\\\"\\\"Calculates 6-month placement rate for a given education type and optional language\\\"\\\"\\\"
    filtered = [r for r in records if r.education == edu_type]
    if lang:
        filtered = [r for r in filtered if r.primary_language == lang]

    if not filtered:
        return 0.0

    placed = [r for r in filtered if r.months_to_placement is not None]
    return (len(placed) / len(filtered)) * 100

def main():
    data_path = \"placement_data_2026.csv\"
    records = load_placement_data(data_path)
    print(f\"Loaded {len(records)} valid candidate records\")

    # Calculate overall rates
    cs_rate = calculate_placement_rate(records, EducationType.CS_DEGREE)
    bootcamp_rate = calculate_placement_rate(records, EducationType.BOOTCAMP)
    print(f\"\\nOverall 6-Month Placement Rates:\")
    print(f\"CS Degree: {cs_rate:.1f}%\")
    print(f\"Bootcamp: {bootcamp_rate:.1f}%\")

    # Calculate language-specific rates
    for lang in Language:
        cs_lang = calculate_placement_rate(records, EducationType.CS_DEGREE, lang)
        boot_lang = calculate_placement_rate(records, EducationType.BOOTCAMP, lang)
        print(f\"\\n{lang.value.upper()} Placement Rates:\")
        print(f\"CS Degree: {cs_lang:.1f}%\")
        print(f\"Bootcamp: {boot_lang:.1f}%\")

if __name__ == \"__main__\":
    main()

// PlacementAnalyzer.java
// Java 23 (OpenJDK 23+37)
// Analyzes placement data for CS degree vs bootcamp graduates
// Requires: Java 23+, CSV file with placement data

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Optional;

// Record to store candidate data (Java 16+ record, but using Java 23 enhancements)
record CandidateRecord(String candidateId, EducationType education, 
                      PrimaryLanguage primaryLanguage, 
                      Optional monthsToPlacement, 
                      Optional firstYearTc) {}

// Enum for education type
enum EducationType {
    CS_DEGREE, BOOTCAMP
}

// Enum for primary language
enum PrimaryLanguage {
    PYTHON_314, JAVA_23
}

public class PlacementAnalyzer {
    // Constants for file parsing
    private static final String CSV_DELIMITER = \",\";
    private static final int EXPECTED_COLUMNS = 5;

    public static void main(String[] args) {
        String filePath = \"placement_data_2026.csv\";
        List records = loadPlacementData(filePath);
        System.out.println(\"Loaded \" + records.size() + \" valid candidate records\");

        // Calculate overall placement rates
        double csRate = calculatePlacementRate(records, EducationType.CS_DEGREE, null);
        double bootcampRate = calculatePlacementRate(records, EducationType.BOOTCAMP, null);
        System.out.printf(\"\\nOverall 6-Month Placement Rates:\\n\");
        System.out.printf(\"CS Degree: %.1f%%\\n\", csRate);
        System.out.printf(\"Bootcamp: %.1f%%\\n\", bootcampRate);

        // Calculate language-specific rates
        for (PrimaryLanguage lang : PrimaryLanguage.values()) {
            double csLangRate = calculatePlacementRate(records, EducationType.CS_DEGREE, lang);
            double bootLangRate = calculatePlacementRate(records, EducationType.BOOTCAMP, lang);
            System.out.printf(\"\\n%s Placement Rates:\\n\", lang);
            System.out.printf(\"CS Degree: %.1f%%\\n\", csLangRate);
            System.out.printf(\"Bootcamp: %.1f%%\\n\", bootLangRate);
        }
    }

    private static List loadPlacementData(String filePath) {
        List records = new ArrayList<>();

        try (BufferedReader br = new BufferedReader(new FileReader(filePath))) {
            String line;
            int rowNum = 0;

            while ((line = br.readLine()) != null) {
                rowNum++;
                // Skip header row
                if (rowNum == 1) {
                    validateHeader(line);
                    continue;
                }

                // Skip empty lines
                if (line.trim().isEmpty()) {
                    continue;
                }

                try {
                    CandidateRecord record = parseCsvRow(line, rowNum);
                    records.add(record);
                } catch (IllegalArgumentException e) {
                    System.err.println(\"Skipping malformed row \" + rowNum + \": \" + e.getMessage());
                }
            }
        } catch (IOException e) {
            System.err.println(\"Error reading file: \" + e.getMessage());
            System.exit(1);
        }

        return records;
    }

    private static void validateHeader(String headerLine) {
        String[] headers = headerLine.split(CSV_DELIMITER);
        List required = List.of(\"candidate_id\", \"education\", \"primary_language\", 
                                        \"months_to_placement\", \"first_year_tc\");
        for (String req : required) {
            boolean found = false;
            for (String h : headers) {
                if (h.trim().equalsIgnoreCase(req)) {
                    found = true;
                    break;
                }
            }
            if (!found) {
                throw new IllegalArgumentException(\"Missing required header: \" + req);
            }
        }
    }

    private static CandidateRecord parseCsvRow(String line, int rowNum) {
        String[] parts = line.split(CSV_DELIMITER, -1); // -1 to include trailing empty strings
        if (parts.length != EXPECTED_COLUMNS) {
            throw new IllegalArgumentException(\"Expected \" + EXPECTED_COLUMNS + \" columns, got \" + parts.length);
        }

        String candidateId = parts[0].trim();
        EducationType edu = EducationType.valueOf(parts[1].trim().toUpperCase());
        PrimaryLanguage lang = PrimaryLanguage.valueOf(parts[2].trim().toUpperCase());

        // Parse months to placement
        Optional months = Optional.empty();
        String mtpStr = parts[3].trim();
        if (!mtpStr.isEmpty()) {
            int mtp = Integer.parseInt(mtpStr);
            if (mtp <= 6) {
                months = Optional.of(mtp);
            }
        }

        // Parse first year TC
        Optional tc = Optional.empty();
        String tcStr = parts[4].trim();
        if (!tcStr.isEmpty()) {
            tc = Optional.of(Integer.parseInt(tcStr));
        }

        return new CandidateRecord(candidateId, edu, lang, months, tc);
    }

    private static double calculatePlacementRate(List records, 
                                                EducationType eduType, 
                                                PrimaryLanguage lang) {
        List filtered = records.stream()
                .filter(r -> r.education() == eduType)
                .filter(r -> lang == null || r.primaryLanguage() == lang)
                .toList();

        if (filtered.isEmpty()) {
            return 0.0;
        }

        long placedCount = filtered.stream()
                .filter(r -> r.monthsToPlacement().isPresent())
                .count();

        return (placedCount / (double) filtered.size()) * 100.0;
    }
}

# hiring_screening.py
# Python 3.14.0-rc2
# Simulates a technical screening for Python 3.14/Java 23 roles
# Includes error handling, benchmarking, and score aggregation

import time
import random
from dataclasses import dataclass
from typing import List, Tuple
from abc import ABC, abstractmethod

@dataclass
class Candidate:
    \\\"\\\"\\\"Stores candidate screening metadata\\\"\\\"\\\"
    candidate_id: str
    education: str  # \"cs_degree\" or \"bootcamp\"
    primary_language: str  # \"python_3.14\" or \"java_23\"
    years_experience: int

class ScreeningQuestion(ABC):
    \\\"\\\"\\\"Abstract base class for screening questions\\\"\\\"\\\"
    @abstractmethod
    def run(self, candidate: Candidate) -> Tuple[int, float]:
        \\\"\\\"\\\"Returns (score 0-100, time_taken_seconds)\\\"\\\"\\\"
        pass

class Python314Question(ScreeningQuestion):
    \\\"\\\"\\\"Python 3.14-specific screening question: implement a type-safe cache decorator\\\"\\\"\\\"
    def run(self, candidate: Candidate) -> Tuple[int, float]:
        start = time.perf_counter()
        # Simulate candidate writing code (in real use, this would execute sandboxed code)
        # For this benchmark, we simulate score based on education and experience
        base_score = 70 if candidate.education == \"cs_degree\" else 60
        exp_bonus = min(candidate.years_experience * 5, 20)
        # Random variance to simulate real performance
        variance = random.randint(-10, 10)
        score = max(0, min(100, base_score + exp_bonus + variance))
        time_taken = random.uniform(120, 600)  # 2-10 minutes to complete
        elapsed = time.perf_counter() - start
        # Simulate actual work time
        time.sleep(min(time_taken / 1000, 0.1))  # Cap sleep for benchmark
        return (score, time_taken)

class Java23Question(ScreeningQuestion):
    \\\"\\\"\\\"Java 23-specific screening question: implement a record pattern matcher\\\"\\\"\\\"
    def run(self, candidate: Candidate) -> Tuple[int, float]:
        start = time.perf_counter()
        base_score = 75 if candidate.education == \"cs_degree\" else 62
        exp_bonus = min(candidate.years_experience * 4, 18)
        variance = random.randint(-8, 8)
        score = max(0, min(100, base_score + exp_bonus + variance))
        time_taken = random.uniform(180, 720)  # 3-12 minutes
        elapsed = time.perf_counter() - start
        time.sleep(min(time_taken / 1000, 0.1))
        return (score, time_taken)

def run_screening(candidates: List[Candidate]) -> dict:
    \\\"\\\"\\\"Runs screening for all candidates, returns aggregated results\\\"\\\"\\\"
    results = {
        \"cs_degree\": {\"python\": [], \"java\": []},
        \"bootcamp\": {\"python\": [], \"java\": []}
    }

    for candidate in candidates:
        try:
            if candidate.primary_language == \"python_3.14\":
                question = Python314Question()
                score, time_taken = question.run(candidate)
                key = \"python\"
            elif candidate.primary_language == \"java_23\":
                question = Java23Question()
                score, time_taken = question.run(candidate)
                key = \"java\"
            else:
                print(f\"Unknown language for candidate {candidate.candidate_id}\")
                continue

            # Store result
            if candidate.education in results:
                results[candidate.education][key].append((score, time_taken))
        except Exception as e:
            print(f\"Error screening candidate {candidate.candidate_id}: {e}\")
            continue

    return results

def aggregate_results(results: dict) -> None:
    \\\"\\\"\\\"Prints aggregated screening results\\\"\\\"\\\"
    for edu_type in [\"cs_degree\", \"bootcamp\"]:
        print(f\"\\n{edu_type.upper()} Screening Results:\")
        for lang in [\"python\", \"java\"]:
            scores = [s for s, t in results[edu_type][lang]]
            times = [t for s, t in results[edu_type][lang]]
            if not scores:
                print(f\"  {lang.upper()}: No candidates\")
                continue
            avg_score = sum(scores) / len(scores)
            avg_time = sum(times) / len(times)
            print(f\"  {lang.upper()}: Avg Score {avg_score:.1f}/100, Avg Time {avg_time:.1f}s\")

def main():
    # Generate simulated candidates (100 CS, 100 bootcamp)
    candidates = []
    for i in range(100):
        # CS degree candidates
        candidates.append(Candidate(
            candidate_id=f\"cs_{i}\",
            education=\"cs_degree\",
            primary_language=random.choice([\"python_3.14\", \"java_23\"]),
            years_experience=random.randint(0, 5)
        ))
    for i in range(100):
        # Bootcamp candidates
        candidates.append(Candidate(
            candidate_id=f\"boot_{i}\",
            education=\"bootcamp\",
            primary_language=random.choice([\"python_3.14\", \"java_23\"]),
            years_experience=random.randint(0, 3)
        ))

    print(f\"Screening {len(candidates)} simulated candidates...\")
    results = run_screening(candidates)
    aggregate_results(results)

if __name__ == \"__main__\":
    main()

# Python 3.14 match statement example for data validation
def validate_payment_request(request: dict) -> bool:
    match request:
        case {\"amount\": float(amt) | int(amt), \"currency\": str(currency), \"user_id\": str(uid)} if amt > 0:
            print(f\"Valid request: {amt} {currency} from {uid}\")
            return True
        case _:
            print(\"Invalid payment request\")
            return False

// Java 23 Foreign Function API example to call a C standard library function
import java.lang.foreign.*;
import java.lang.invoke.MethodHandle;

public class FFMExample {
    public static void main(String[] args) throws Throwable {
        // Get the C standard library linker
        Linker linker = Linker.nativeLinker();
        SymbolLookup stdlib = linker.defaultLookup();

        // Find the puts function from the C standard library
        MemorySegment puts = stdlib.find(\"puts\").orElseThrow();

        // Create a method handle for puts (takes a C string, returns int)
        MethodHandle putsHandle = linker.downcallHandle(
            puts,
            FunctionDescriptor.of(ValueLayout.JAVA_INT, ValueLayout.ADDRESS)
        );

        // Allocate a C string \"Hello from Java 23!\"
        Arena arena = Arena.ofConfined();
        MemorySegment cString = arena.allocateUtf8String(\"Hello from Java 23!\");

        // Call puts
        putsHandle.invoke(cString);
        arena.close();
    }
}

# Python 3.14 FastAPI example with @override decorator
from fastapi import FastAPI
from typing import override

app = FastAPI()

class BasePaymentProcessor:
    def process_payment(self, amount: float, currency: str) -> dict:
        raise NotImplementedError(\"Subclasses must implement this\")

class StripeProcessor(BasePaymentProcessor):
    @override  # Python 3.14 @override decorator enforces correct method signature
    def process_payment(self, amount: float, currency: str) -> dict:
        # Simulate Stripe API call
        return {\"status\": \"success\", \"amount\": amount, \"currency\": currency, \"processor\": \"stripe\"}

@app.post(\"/payments\")
def create_payment(amount: float, currency: str):
    processor = StripeProcessor()
    return processor.process_payment(amount, currency)