2. 2
Classical Waterfall Model
●
Classical waterfall model divides life cycle
into phases:
– feasibility study,
– requirements analysis and specification,
– design,
– coding and unit testing,
– integration and system testing,
– maintenance.
4. 4
Relative Effort for Phases
●
Phases between feasibility
study and testing
– known as development phases.
●
Among all life cycle phases
Among all life cycle phases
– maintenance phase consumes
maintenance phase consumes
maximum effort.
maximum effort.
●
Among development phases,
– testing phase consumes the
maximum effort. 0
10
20
30
40
50
60
Req.
Sp
Design
Coding
Test
Maintnce
Relative Effort
5. 5
Classical Waterfall Model
(CONT.)
●
Most organizations usually define:
– standards on the outputs (deliverables) produced
at the end of every phase
– entry and exit criteria for every phase.
●
They also prescribe specific methodologies for:
– specification,
– design,
– testing,
– project management, etc.
6. 6
Classical Waterfall Model
(CONT.)
●
The guidelines and methodologies of an
organization:
– called the organization's software
software
development methodology
development methodology.
.
●
Software development organizations:
– expect fresh engineers to master the
organization's software development
methodology.
7. 7
Feasibility Study
●
Main aim of feasibility study:determine whether
developing the product
– financially worthwhile
– technically feasible.
●
First roughly understand what the customer wants:
– different data which would be input to the system,
– processing needed on these data,
– output data to be produced by the system,
– various constraints on the behavior of the system.
8. 8
Activities during Feasibility
Study
●
Work out an overall understanding of the
problem.
●
Formulate different solution strategies.
●
Examine alternate solution strategies in
terms of:
●
resources required,
●
cost of development, and
●
development time.
9. 9
Activities during Feasibility
Study
●
Perform a cost/benefit analysis:
– to determine which solution is the best.
– you may determine that none of the
solutions is feasible due to:
●
high cost,
●
resource constraints,
●
technical reasons.
10. 10
Requirements Analysis and
Specification
●
Aim of this phase:
– understand the exact requirements of
the customer,
– document them properly.
●
Consists of two distinct activities:
– requirements gathering and analysis
– requirements specification.
11. 11
Goals of Requirements Analysis
●
Collect all related data from the
customer:
– analyze the collected data to clearly
understand what the customer wants,
– find out any inconsistencies and
incompleteness in the requirements,
– resolve all inconsistencies and
incompleteness.
12. 12
Requirements Gathering
●
Gathering relevant data:
– usually collected from the end-users
through interviews and discussions.
– For example, for a business accounting
software:
●
interview all the accountants of the
organization to find out their requirements.
13. 13
Requirements Analysis (CONT.)
●
The data you initially collect from
the users:
– would usually contain several
contradictions and ambiguities:
– each user typically has only a
partial and incomplete view of the
system.
14. 14
Requirements Analysis (CONT.)
●
Ambiguities and contradictions:
– must be identified
– resolved by discussions with the customers.
●
Next, requirements are organized:
– into a Software Requirements Specification
(SRS) document.
17. 17
Design
●
In technical terms:
– during design phase, software
architecture is derived from the SRS
document.
●
Two design approaches:
– traditional approach,
– object oriented approach.
19. 19
Structured Analysis Activity
●
Identify all the functions to be
performed.
●
Identify data flow among the functions.
●
Decompose each function recursively into
sub-functions.
– Identify data flow among the subfunctions as
well.
20. 20
Structured Analysis (CONT.)
●
Carried out using Data flow diagrams
(DFDs).
●
After structured analysis, carry out
structured design:
– architectural design (or high-level
design)
– detailed design (or low-level design).
21. 21
Structured Design
●
High-level design:
– decompose the system into modules,
– represent invocation relationships among the
modules.
●
Detailed design:
– different modules designed in greater detail:
●
data structures and algorithms for each module are
designed.
22. 22
Object Oriented Design
●
First identify various objects (real world
entities) occurring in the problem:
– identify the relationships among the objects.
– For example, the objects in a pay-roll software
may be:
●
employees,
●
managers,
●
pay-roll register,
●
Departments, etc.
23. 23
Object Oriented Design (CONT.)
●
Object structure
– further refined to obtain the detailed
design.
●
OOD has several advantages:
– lower development effort,
– lower development time,
– better maintainability.
25. 25
Implementation
●
During the implementation phase:
– each module of the design is coded,
– each module is unit tested
●
tested independently as a stand alone unit,
and debugged,
– each module is documented.
26. 26
Implementation (CONT.)
●
The purpose of unit testing:
– test if individual modules work correctly.
●
The end product of implementation
phase:
– a set of program modules that have been
tested individually.
27. 27
Integration and System
Testing
●
Different modules are integrated in a
planned manner:
– modules are almost never integrated in one
shot.
– Normally integration is carried out through a
number of steps.
●
During each integration step,
– the partially integrated system is tested.
29. 29
System Testing
●
After all the modules have been
successfully integrated and tested:
– system testing is carried out.
●
Goal of system testing:
– ensure that the developed system
functions according to its
requirements as specified in the SRS
document.
30. 30
Maintenance
●
Maintenance of any software
product:
– requires much more effort than
the effort to develop the product
itself.
– development effort to
maintenance effort is typically
40:60.
31. 31
Maintenance (CONT.)
●
Corrective maintenance:
– Correct errors which were not discovered during the
product development phases.
●
Perfective maintenance:
– Improve implementation of the system
– enhance functionalities of the system.
●
Adaptive maintenance:
– Port software to a new environment,
●
e.g. to a new computer or to a new operating system.
32. 32
Iterative Waterfall Model
●
Classical waterfall model is idealistic:
– assumes that no defect is introduced
during any development activity.
– in practice:
●
defects do get introduced in almost every
phase of the life cycle.
34. 34
Iterative Waterfall Model
(CONT.)
●
Once a defect is detected:
– we need to go back to the phase where it was
introduced
– redo some of the work done during that and
all subsequent phases.
●
Therefore we need feedback paths in the
classical waterfall model.
36. 36
Iterative Waterfall Model
(CONT.)
●
Errors should be detected
in the same phase in which they are
introduced.
●
For example:
if a design problem is detected in the
design phase itself,
the problem can be taken care of much
more easily
than say if it is identified at the end of
the integration and system testing phase.
37. 37
Phase containment of errors
●
Reason: rework must be carried out not only to
the design but also to code and test phases.
●
The principle of detecting errors as close to its
point of introduction as possible:
– is known as phase containment of errors.
●
Iterative waterfall model is by far the most
widely used model.
– Almost every other model is derived from the
waterfall model.
38. 38
Classical Waterfall Model
(CONT.)
●
Irrespective of the life cycle model
actually followed:
– the documents should reflect a classical
waterfall model of development,
– comprehension of the documents is
facilitated.
39. 39
Classical Waterfall Model
(CONT.)
●
Metaphor of mathematical theorem
proving:
– A mathematician presents a proof as a
single chain of deductions,
●
even though the proof might have come
from a convoluted set of partial attempts,
blind alleys and backtracks.
40. 40
Prototyping Model
●
Before starting actual development,
– a working prototype of the system should first
be built.
●
A prototype is a toy implementation of a
system:
– limited functional capabilities,
– low reliability,
– inefficient performance.
41. 41
Reasons for developing a
prototype
●
Illustrate to the customer:
– input data formats, messages,
reports, or interactive dialogs.
●
Examine technical issues associated
with product development:
– Often major design decisions depend
on issues like:
●
response time of a hardware controller,
●
efficiency of a sorting algorithm, etc.
42. 42
Prototyping Model (CONT.)
●
The third reason for developing a
prototype is:
– it is impossible to ``get it right'' the
first time,
– we must plan to throw away the first
product
●
if we want to develop a good product.
43. 43
Prototyping Model (CONT.)
●
Start with approximate requirements.
●
Carry out a quick design.
●
Prototype model is built using several
short-cuts:
– Short-cuts might involve using inefficient,
inaccurate, or dummy functions.
●
A function may use a table look-up rather than
performing the actual computations.
44. 44
Prototyping Model (CONT.)
●
The developed prototype is submitted to
the customer for his evaluation:
– Based on the user feedback, requirements
are refined.
– This cycle continues until the user approves
the prototype.
●
The actual system is developed using the
classical waterfall approach.
46. 46
Prototyping Model (CONT.)
●
Requirements analysis and specification phase
becomes redundant:
– final working prototype (with all user feedbacks
incorporated) serves as an animated requirements
specification.
●
Design and code for the prototype is usually thrown
away:
– However, the experience gathered from developing the
prototype helps a great deal while developing the actual
product.
47. 47
Prototyping Model (CONT.)
●
Even though construction of a working prototype
model involves additional cost --- overall
development cost might be lower for:
– systems with unclear user requirements,
– systems with unresolved technical issues.
●
Many user requirements get properly defined and
technical issues get resolved:
– these would have appeared later as change
requests and resulted in incurring massive redesign
costs.
48. 48
Evolutionary Model
●
Evolutionary model (aka successive versions or
incremental model):
– The system is broken down into several modules which
can be incrementally implemented and delivered.
●
First develop the core modules of the system.
●
The initial product skeleton is refined into
increasing levels of capability:
– by adding new functionalities in successive versions.
49. 49
Evolutionary Model (CONT.)
●
Successive version of the product:
– functioning systems capable of
performing some useful work.
– A new release may include new
functionality:
●
also existing functionality in the current
release might have been enhanced.
51. 51
Advantages of Evolutionary
Model
●
Users get a chance to experiment with a partially
developed system:
– much before the full working version is released,
●
Helps finding exact user requirements:
– much before fully working system is developed.
●
Core modules get tested thoroughly:
– reduces chances of errors in final product.
52. 52
Disadvantages of
Evolutionary Model
●
Often, difficult to subdivide problems
into functional units:
– which can be incrementally implemented
and delivered.
– evolutionary model is useful for very
large problems,
●
where it is easier to find modules for
incremental implementation.
53. 53
Evolutionary Model with
Iteration
●
Many organizations use a combination
of iterative and incremental
development:
– a new release may include new
functionality
– existing functionality from the current
release may also have been modified.
54. 54
Evolutionary Model with
iteration
●
Several advantages:
– Training can start on an earlier release
●
customer feedback taken into account
– Markets can be created:
●
for functionality that has never been offered.
– Frequent releases allow developers to fix
unanticipated problems quickly.
55. 55
Spiral Model
●
Proposed by Boehm in 1988.
●
Each loop of the spiral represents a phase of the
software process:
– the innermost loop might be concerned with system
feasibility,
– the next loop with system requirements definition,
– the next one with system design, and so on.
●
There are no fixed phases in this model, the
phases shown in the figure are just examples.
56. 56
Spiral Model (CONT.)
●
The team must decide:
– how to structure the project into phases.
●
Start work using some generic model:
– add extra phases
●
for specific projects or when problems are
identified during a project.
●
Each loop in the spiral is split into four
sectors (quadrants).
58. 58
Objective Setting (First
Quadrant)
●
Identify objectives of the phase,
●
Examine the risks associated with these
objectives.
– Risk:
●
any adverse circumstance that might
hamper successful completion of a
software project.
●
Find alternate solutions possible.
59. 59
Risk Assessment and Reduction (Second
Quadrant)
●
For each identified project risk,
– a detailed analysis is carried out.
●
Steps are taken to reduce the risk.
●
For example, if there is a risk that the
requirements are inappropriate:
– a prototype system may be developed.
60. 60
Spiral Model (CONT.)
●
Development and Validation (Third quadrant
Third quadrant):
– develop and validate the next level of the product.
●
Review and Planning (Fourth quadrant
Fourth quadrant):
– review the results achieved so far with the customer
and plan the next iteration around the spiral.
●
With each iteration around the spiral:
– progressively more complete version of the software
gets built.
61. 61
Spiral Model as a meta
model
●
Subsumes all discussed models:
– a single loop spiral represents waterfall model.
– uses an evolutionary approach --
●
iterations through the spiral are evolutionary levels.
– enables understanding and reacting to risks
during each iteration along the spiral.
– uses:
●
prototyping as a risk reduction mechanism
●
retains the step-wise approach of the waterfall model.
62. 62
Comparison of Different Life
Cycle Models
●
Iterative waterfall model
– most widely used model.
– But, suitable only for well-understood
problems.
●
Prototype model is suitable for projects
not well understood:
– user requirements
– technical aspects
63. 63
Comparison of Different Life
Cycle Models (CONT.)
●
Evolutionary model is suitable for large
problems:
– can be decomposed into a set of modules
that can be incrementally implemented,
– incremental delivery of the system is
acceptable to the customer.
●
The spiral model:
– suitable for development of technically
challenging software products that are
subject to several kinds of risks.
