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SCS2110 | Module 8: Report Outlines, Ethics & Proposal
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Module 8: Report Outlines, Research Ethics & Proposal Drafting

Directly targets **DECEMBER 2024 EXAM QUESTIONS ONE [20 MARKS], THREE [20 MARKS], AND SIX [20 MARKS]**. Master project chapter structures, openEuler char driver experiment reporting, proposal drafting, and academic ethics.

Part 1: Final Year Research Project Report Structure (Dec 2024 Q1)
M8-Q01 [DEC 2024 EXAM Q1] [DRAFT & OUTLINE]
Give a detailed outline of the FIVE chapters in a final year CS research project report. [20 Marks - 4 Marks per chapter]
NUST Model Answer [20 Marks Breakdown]:
  • Chapter One: Introduction [4 Marks]:
    Provides high-level background, research context, formal Problem Statement, Research Aim (The "What"), Research Objectives (The "How"), Research Questions/Hypotheses, Delimitations, Significance of study, and Report Organization overview.
  • Chapter Two: Literature Review [4 Marks]:
    Presents a systematic review (SLR) of existing academic literature, historical context, current state-of-the-art solutions, comparative evaluation of past approaches, PRISMA flow diagram, identification of research knowledge gaps, and theoretical framework.
  • Chapter Three: Methodology [4 Marks]:
    Details the research paradigm (Positivism/Pragmatism), research methodology (Applied Experimental/Mixed Methods), software development methodology (Iterative Prototyping/Agile justification), target population, sampling strategy, data collection instruments, environment controls, and ethical considerations.
  • Chapter Four: Analysis and Design [4 Marks]:
    Presents system requirements analysis, Context Diagram (current baseline), Use Case Diagrams (proposed system), Activity Diagrams, 3-Tier System Architecture, ERD Database Schema design, mathematical algorithm formulation, and pseudocode.
  • Chapter Five: Implementation and Testing [4 Marks]:
    Documents hardware/software implementation environment, core code module integration, benchmarking experiment execution, empirical results presentation (graphs/tables), statistical validation (t-test/ANOVA), error rate analysis, comparison against baselines, and project conclusions/future work.
Part 2: Reporting Experiments (Dec 2024 Q3)
M8-Q02 [DEC 2024 EXAM Q3] [APPLY & SOLVE]
Demonstrate your research writing skill by reporting on the openEuler operating system char driver experiment performed during class. [20 Marks]
NUST Model Answer [20 Marks Section Breakdown]:

1. Abstract / Introduction (4 Marks):
Briefly state the goal: evaluating character driver kernel module memory allocation and I/O latency under openEuler OS.

2. Experimental Setup & Controls (5 Marks):
Specify hardware (e.g. QEMU ARM64/x86_64, 4GB RAM), openEuler kernel version, gcc flags, and controlled variables (CPU frequency locked, no background daemon activity).

3. Method & Device Registration (5 Marks):
Detail driver registration via alloc_chrdev_region(), cdev_init(), custom file_operations struct (read/write/ioctl routines), and ring buffer allocation.

4. Empirical Results & Discussion (6 Marks):
Present a benchmark comparison table measuring read/write throughput (MB/s) and context switch latency ($\mu s$) across variable payload sizes (64B to 64KB). Conclude with validity discussion and memory safety evaluation.
Part 3: Research Ethics & "Cardinal Sins"
M8-Q03 [CRITIQUE & AUDIT]
Explain the FIVE "Cardinal Sins" of academic research ethics in Computer Science and state how to prevent each. [10 Marks]
NUST Model Answer [10 Marks]:
  • 1. Data Fabrication: Making up fake benchmark results or survey responses. Prevention: Retain raw timestamped execution logs for 5 years.
  • 2. Data Falsification: Manipulating hardware logs, deleting outliers selectively, or falsifying performance benchmarks to match a hypothesis. Prevention: Report all data points transparently with standard deviation and confidence intervals.
  • 3. Plagiarism & Idea Theft: Copying text, code, or ideas from others without proper Harvard citation. Prevention: Always cite original authors and run Turnitin audits before submission.
  • 4. AI Hallucination & Uncited AI Use: Copying fake AI-generated references or unverified code snippets into a paper. Prevention: Paraphrase using human judgment and verify all references in academic databases.
  • 5. Unethical Human Data Collection: Gathering student biometric or personal network data without Institutional Review Board (IRB) ethical clearance. Prevention: Obtain formal written consent and anonymize all participant data.