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A14.1 The systems lifecycle and feasibility

Software development, law and ethics · A level · OCR H446 1.2.3, AQA 7517 4.13.1.1, Eduqas A500QS 1.5 · about 35 min

BugBotLab

What this lesson is about

The stages from feasibility to maintenance, the three kinds of maintenance, and scoring a feasibility study.

Questions 6 marks in all

  1. [1 mark]Put these stages of the systems lifecycle in order.

    Number the lines 1 to 6 to put them in the right order.

    1. Feasibility study
    2. Testing
    3. Analysis
    4. Implementation
    5. Evaluation
    6. Design
    Answer:
    Feasibility study
Analysis
Design
Implementation
Testing
Evaluation

    Each stage uses what the one before produced: the requirements from analysis are designed, built, tested and finally evaluated.

  2. [1 mark]What is the purpose of a feasibility study?

    1. ATo decide whether the project is possible and worth doing before money is spent on it
    2. BTo find and fix the errors in the finished program
    3. CTo write the program's user documentation
    4. DTo train the users on the new system
    Answer: A. A feasibility study weighs technical, economic, legal, operational and schedule factors and recommends whether to go ahead.

  3. [1 mark]A school opens a new wing, so the delivery robot's software is changed to include the new corridors. What kind of maintenance is this?

    1. AAdaptive
    2. BCorrective
    3. CPerfective
    4. DDestructive
    Answer: A. Adaptive maintenance changes a system because its environment has changed; corrective fixes errors and perfective improves it.

  4. [1 mark]Which of these are aspects considered in a feasibility study?

    Tick every answer that is true.

    1. ALegal
    2. BEconomic
    3. CSchedule
    4. DSyntax
    5. EPair programming
    Answer: A, B, C. TELOS: technical, economic, legal, operational and schedule. Syntax and pair programming belong to implementation.

  5. [1 mark]Against what should a finished system be evaluated?

    1. AThe success criteria agreed during analysis
    2. BHow much code was written
    3. CWhether the developers enjoyed building it
    4. DThe number of tests that were run
    Answer: A. Evaluation checks each agreed criterion with evidence, and considers qualities such as usability and maintainability.

  6. [1 mark]A feasibility study scores each aspect and weights it. What does this print?

    aspects = [("technical", 4, 3), ("economic", 2, 2), ("legal", 5, 1)]
total = sum(s * w for _, s, w in aspects)
weights = sum(w for _, _, w in aspects)
print(total, weights, round(total / weights, 1))
    Answer:
    21 6 3.5

    The total is 12 + 4 + 5 = 21 over weights 3 + 2 + 1 = 6, giving 3.5.

The task: the feasibility study

Four robot projects have been scored for feasibility. proposals is a list of (name, aspects), where name is a string and aspects is a list of five tuples (aspect, score, weight): aspect is a string, score is a whole number from 0 to 5, and weight is a whole number from 1 to 3. Write two functions: - weighted_score(aspects) returns the total of score * weight divided by the total of the weights, rounded to one decimal place. - blockers(aspects) returns a list of the names of the aspects that score less than 2, in the order they appear. Then, for each proposal in order, print one line: <name>: <score> <verdict>, with the score to one decimal place. The verdict is not feasible (blocked by <aspects>) if there are any blockers, with their names separated by , ; otherwise not feasible (score below 3) if the score is less than 3; otherwise feasible. For example, delivery robot: 3.5 feasible. The robot stays still.

# the two lines every program starts with: the commands, then the robot
from bugbot import *
connect()

proposals = [
    ("delivery robot", [("technical", 4, 3), ("economic", 3, 2), ("legal", 4, 2), ("operational", 3, 2), ("schedule", 3, 1)]),
    ("face-recognition door", [("technical", 4, 3), ("economic", 4, 2), ("legal", 1, 2), ("operational", 3, 2), ("schedule", 4, 1)]),
    ("robot lawnmower fleet", [("technical", 3, 3), ("economic", 2, 2), ("legal", 3, 2), ("operational", 3, 2), ("schedule", 2, 1)]),
    ("voice-controlled lift", [("technical", 1, 3), ("economic", 1, 2), ("legal", 4, 2), ("operational", 3, 2), ("schedule", 4, 1)]),
]

def weighted_score(aspects):
    pass

def blockers(aspects):
    pass

The hint students can ask for: The weighted score is the total of score times weight, divided by the total of the weights. Find the blockers first, because a proposal with a blocker fails whatever its score; only then compare the score with 3.

A solution

# the two lines every program starts with: the commands, then the robot
from bugbot import *
connect()

proposals = [
    ("delivery robot", [("technical", 4, 3), ("economic", 3, 2), ("legal", 4, 2), ("operational", 3, 2), ("schedule", 3, 1)]),
    ("face-recognition door", [("technical", 4, 3), ("economic", 4, 2), ("legal", 1, 2), ("operational", 3, 2), ("schedule", 4, 1)]),
    ("robot lawnmower fleet", [("technical", 3, 3), ("economic", 2, 2), ("legal", 3, 2), ("operational", 3, 2), ("schedule", 2, 1)]),
    ("voice-controlled lift", [("technical", 1, 3), ("economic", 1, 2), ("legal", 4, 2), ("operational", 3, 2), ("schedule", 4, 1)]),
]

def weighted_score(aspects):
    total = sum(score * weight for _, score, weight in aspects)
    weights = sum(weight for _, _, weight in aspects)
    return round(total / weights, 1)

def blockers(aspects):
    return [name for name, score, _ in aspects if score < 2]

for name, aspects in proposals:
    score = weighted_score(aspects)
    blocked = blockers(aspects)
    if blocked:
        verdict = "not feasible (blocked by " + ", ".join(blocked) + ")"
    elif score < 3:
        verdict = "not feasible (score below 3)"
    else:
        verdict = "feasible"
    print(f"{name}: {score:.1f} {verdict}")

Any program that meets the task's checks is marked correct in the simulator; this is one way, not the only way.