Comprehensive dataset containing 33 verified Assignment Help From No1AssignmentHelp.Com locations in Australia with complete contact information, ratings, reviews, and location data.

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This data set was used to perform the analyses of the article entitled 'Concept Based Auto-Assignment of Healthcare Questions to Domain Experts in Online Q&A Communities' submitted to international journal of medical informatics.

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RSVP-SQL-ASSIGNMENT

RSVP Movies is an Indian film production company which has produced many super-hit movies. They have usually released movies for the Indian audience but for their next project, they are planning to release a movie for the global audience in 2023.

The production company wants to plan their every move analytically based on data and have approached you for help with this new project. You have been provided with the data of the movies that have been released in the past three years. You have to analyse the data set and draw meaningful insights that can help them start their new project.

You are a data analyst and an SQL expert. You have to use SQL to analyse the given data and give recommendations to RSVP Movies based on the insights. For your convenience, the entire analytics process has been divided into four segments, where each segment leads to significant insights from different combinations of tables. The questions in each segment with business objectives are written in the script given below. You have to write the solution code below every question.

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This feature class is updated every business day using Python scripts and the Permit database. Please disregard the "Date Updated" field as it does not keep in sync with DWR's internal enterprise geodatabase updates. Water Right Specialists, also called Basin Engineers, manage the water rights in their assigned basins across the state. This dataset was designed to provide the public with an easy to use map showing the preferred DWR employee to contact for questions about water rights in a specific area of interest. This dataset is used for the Basin Specialist Assignment wall map and the Water Rights web map. For contact information, please see water.nv.gov/contactinformation.aspxBackground Info on Basins:The U.S. Geological Survey and the Nevada Division of Water Resources divided the state into discrete hydrologic units for water planning and management purposes. These administrative boundaries have been identified as 232 Hydrographic Basins (1-232; hydrographic sub-basins designated A, B, C, etc.) within 14 major Hydrographic Regions. There are a total of 256 hydrographic basins and sub-basins.

Exploring Large Language Models for Automated Non-Functional Requirements Generation: A Human Annotated Dataset for NFR Quality

This artifact provides a comprehensive dataset and analysis tools for evaluating the quality of Non-Functional Requirements (NFRs) generated by Large Language Models (LLMs) based solely on Functional Requirements (FRs). The dataset includes human evaluations of NFR quality according to ISO/IEC 25010:2023 standard quality attributes.

Description

This research artifact contains:

• Human evaluation data for NFRs generated by 8 different LLMs across 34 functional requirements
• Professional responses collected through Turso database service from software engineering professionals
• Analysis scripts for data processing and statistical analysis
• LLM outputs in structured JSON format for all tested models
• Advanced prompting techniques incorporating ISO/IEC 25010:2023 standards

The study evaluates two key aspects:

1. NFR Validity (1-5 scale): Coherence and appropriateness of generated NFRs
2. Attribute Applicability (1-5 scale): Relevance of assigned ISO quality attributes

Requirements

Software Dependencies

• Deno (JavaScript/TypeScript runtime) - Version 1.40+ recommended
• SQLite3 (for database operations)
• Standard text editor (for viewing TSV/JSON files)

Hardware Requirements

• RAM: Minimum 4GB (recommended 8GB)
• Storage: 500MB free space
• OS: Cross-platform (Linux, macOS, Windows)

Installation

1. Install Deno:

# Linux/macOS
curl -fsSL https://deno.land/install.sh | sh

# Windows (PowerShell)
irm https://deno.land/install.ps1 | iex

1. Verify Installation:

deno --version

1. Clone/Download Artifact: Extract downloaded archive

Step-by-Step Instructions to Reproduce Paper Results

Step 1: Examine Raw Data Sources

Input: Professional evaluation data collected via Turso database service

• File: data/dump.sql
• Description: SQL dump containing responses from software engineering professionals who evaluated LLM-generated NFRs
• Content: Raw evaluation data including validity scores (1-5), applicability scores (1-5), and quality attribute assignments

Expected Output: Understanding of data collection methodology and raw response structure

Step 2: Generate Analysis Database

Purpose: Convert SQL dump to SQLite database for analysis

cd analysis
deno run --allow-read --allow-write generateData.ts

Process:

• The generateData.ts script reads data/dump.sql
• Creates data/dump.db SQLite database
• Structures data for statistical analysis

Expected Output: data/dump.db file created (approximately 2-5MB)

Step 3: Process and Merge Evaluation Data

Purpose: Combine human evaluations with LLM assignments and generate final dataset

The generateData.ts script performs:

1. Assignment Processing: Maps evaluators to specific FR-LLM combinations:
   • NFR Validity evaluations: 10 evaluators × 3 FRs each × 8 LLMs
   • Attribute Applicability evaluations: 10 evaluators × 3 FRs each × 8 LLMs
2. Data Merging: Combines database records with assignment metadata
3. CSV Generation: Outputs structured TSV file for analysis

Expected Output: analysis/Human_Evaluation_Data.tsv (final dataset used in paper)

Step 4: Analyze LLM Output Structure

Files: LLMOutputs/*.json (8 files, one per LLM)

• claude-3-5-haiku.json
• claude-3-7-sonnet.json
• deepSeek-V3.json
• gemini-1.5-pro.json
• gpt-4o-mini.json
• grok-2.json
• lama-3.3-70B.json
• Qwen2.5-72B.json

Expected Format for each FR:

{
 "functionalRequirement": "System shall allow users to log in with username and password",
 "identifiedNFRs": [
  {
   "attribute": "Security",
   "requirement": "The system must encrypt passwords using AES-256 encryption",
   "justification": "Login functionality requires secure credential handling"
  }
 ]
}

Analysis: Each JSON contains 34 FR entries with generated NFRs following ISO/IEC 25010:2023 categories

Step 5: Examine Prompt Engineering Approach

File: data/AdvancedPrompt.txt Content: Complete prompt used for NFR generation including:

• Role assignment (expert software quality engineer)
• Knowledge grounding (ISO/IEC 25010:2023 standard)
• Output structure constraints (JSON format)
• Quality requirements (specific, actionable, testable NFRs)

File Descriptions

Core Dataset Files

• analysis/Human_Evaluation_Data.tsv: Main evaluation dataset (2,240 evaluated NFRs)
  • Columns: FR ID, FR text, NFR ID, LLM model, ISO attribute, NFR text, justification, validity score, applicability score, human attribute assignment, evaluator assignment type, evaluator ID
• data/FR_34.tsv: 34 functional requirements subset used for evaluation
• data/dump.sql: Raw SQL dump from Turso database service containing professional evaluations

LLM Output Files

• LLMOutputs/[model].json: Structured NFR generations for each of 8 LLMs
  • Each file contains 34 FR entries with associated NFRs in JSON format

Configuration Files

• data/AdvancedPrompt.txt: Complete prompt template with ISO/IEC 25010:2023 integration
• analysis/generateData.ts: Data processing script for database creation and CSV generation

Documentation

• LICENSE.md: Distribution rights and usage terms
• analysis/visualization.ipynb: Jupyter notebook for data visualization and statistical analysis

Mapping to Paper Claims

Key Paper Statistics (Section 6 - Results)

• 1,593 total NFRs generated across 8 LLMs and 34 FRs
• 174 NFRs evaluated for validity and applicability scoring
• 168 NFRs evaluated for attribute selection task
• Mean validity score: 4.63 (median: 5.0) on 1-5 scale
• Mean applicability score: 4.59 (median: 5.0) on 1-5 scale
• 80.4% attribute accuracy in expert vs. LLM attribute selection

Figure Reproduction Mapping

• Figure 3: Reproduced from validity scores in Human_Evaluation_Data.tsv
  • Shows 90.8% of NFRs scored ≥4, with 76.4% scoring perfect 5
• Figure 4: Generated from applicability scores in Human_Evaluation_Data.tsv
  • Demonstrates 90.2% highly applicable ratings (scores 4-5)
• Figure 5: Computed from attribute selection task data
  • Visualizes 80.4% exact matches, 8.3% near misses, 11.3% complete mismatches
• Figure 6: Generated from LLM vs. expert attribute assignments
  • Shows specific misclassification patterns (e.g., Functional Suitability vs. Reliability)

Table Reproduction Mapping

• Table 4 (LLM Comparison): Directly derived from Human_Evaluation_Data.tsv grouped by LLM model
  • Validity ranges: 3.96 (claude-3-7-sonnet) to 4.94 (llama-3.3-70B)
  • Applicability ranges: 3.67 (claude-3-7-sonnet) to 4.97 (grok-2)
  • Attribute accuracy ranges: 71.4% (deepSeek-V3) to 90.9% (gemini-1.5-pro)

Research Questions Validation

• RQ1 (LLM Effectiveness): Validated through high validity (90.8% ≥4) and applicability (90.2% ≥4) scores
• RQ2 (Best Performing LLM): Answered via Table 4 comparison showing gemini-1.5-pro (highest attribute accuracy) and llama-3.3-70B (highest validity/applicability)
• RQ3 (Prompting Technique Impact): Demonstrated through advanced vs. baseline prompting comparison

Methodology Reproduction (Section

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