Experience: MC0084 solved assignment

Master of Computer Application (MCA) – Semester 5

MC0084 – Software Project Management &

Quality Assurance – 4 Credits

(Book ID: B0958 & B0959)

Assignment Set – 1 (60 Marks)

______________________________________________________________________________

1. What is project management? Explain various activities involved in project management.

Ans:

Project management is a systematic method of defining and achieving targets with optimized use of resources such as time, money, manpower, material, energy, and space. It is an application of knowledge, skills, resources, and techniques to meet project requirements. Project management involves various activities, which are as follows:

Work planning
Resource estimation
Organizing the work
Acquiring recourses such as manpower, material, energy, and space
Risk assessment
Task assigning
Controlling the project execution
Reporting the progress
Directing the activities
Analyzing the results

2. Describe the following with respect to Estimation and Budgeting of Projects:

a. Software Cost Estimation and Methods

b. COCOMO model and its variations

Ans:

a) Software Cost Estimation and Methods

A number of methods have been used to estimate software cost.

Algorithmic Models

These methods provide one or more algorithms which produce a software cost estimate as a function of a number of variables which relate to some software metric (usually its size) and cost drivers.

Expert Judgment

This method involves consulting one or more experts, perhaps with the aid of an expert-consensus mechanism such as the Delphi technique

Analogy Estimation

This method involves reasoning by analogy with one or more completed projects to relate their actual costs to an estimate of the cost of a similar new project.

Top-Down Estimation

An overall cost estimate for the project is derived from global properties of the software product. The total cost is then split up among the various components.

Bottom-Up Estimation

Each component of the software job is separately estimated, and the results aggregated to produce an estimate for the overall job.

Parkinson's Principle

A Parkinson principle ('Work expands to fill the available volume") is invoked to equate the cost estimate to the available resources.

Price to Win

The cost estimation developed by this method is equated to the price believed necessary to win the job. The estimated effort depends on the customer's budget and not on the software functionality

Bottom-Up Estimation

Each component of the software job is separately estimated,and the results aggregated to produce an estimate for the overall job.

Cost Estimation Guidelines

Assign the initial estimating task to the final developers.

Delay finalizing the initial estimate until the end of a thorough study.

Anticipate and control user changes.

Monitor the progress of the proposed project.

Evaluate proposed project progress by using independent auditors.

Use the estimate to evaluate project personnel.

Computing management should carefully approve the cost estimate.

Rely on documented facts, standards, and simple arithmetic formulas rather than guessing, intuition, personal memory, and complex formulas.

Don't rely on cost estimating software for an accurate estimate.

b) COCOMO model and its variations

The Constructive Cost Model (COCOMO) is an algorithmic software cost estimation model developed by Barry Boehm. The model uses a basic regression formula, with parameters that are derived from historical project data and current project characteristics.

COCOMO was first published in 1981 Barry W. Boehm's Book Software engineering economics[1] as a model for estimating effort, cost, and schedule for software projects. It drew on a study of 63 projects at TRW Aerospace where Barry Boehm was Director of Software Research and Technology in 1981. The study examined projects ranging in size from 2,000 to 100,000 lines of code, and programming languages ranging from assembly to PL/I. These projects were based on the waterfall model of software development which was the prevalent software development process in 1981.

References to this model typically call it COCOMO 81. In 1997 COCOMO II was developed and finally published in 2000 in the book Software Cost Estimation with COCOMO II[2]. COCOMO II is the successor of COCOMO 81 and is better suited for estimating modern software development projects. It provides more support for modern software development processes and an updated project database. The need for the new model came as software development technology moved from mainframe and overnight batch processing to desktop development, code reusability and the use of off-the-shelf software components. This article refers to COCOMO 81.

COCOMO consists of a hierarchy of three increasingly detailed and accurate forms. The first level, Basic COCOMO is good for quick, early, rough order of magnitude estimates of software costs, but its accuracy is limited due to its lack of factors to account for difference in project attributes (Cost Drivers). Intermediate COCOMO takes these Cost Drivers into account and Detailed COCOMO additionally accounts for the influence of individual project phases.

COCOMO applies to three classes of software projects:

* Organic projects - "small" teams with "good" experience working with "less than rigid" requirements

* Semi-detached projects - "medium" teams with mixed experience working with a mix of rigid and less than rigid requirements

* Embedded projects - developed within a set of "tight" constraints (hardware, software, operational, ...)

The basic COCOMO equations take the form

Effort Applied = ab(KLOC)bb [ man-months ]

Development Time = cb(Effort Applied)db [months]

People required = Effort Applied / Development Time [count]

The coefficients ab, bb, cb and db are given in the following table.

Software project ab bb cb db

Organic 2.4 1.05 2.5 0.38

Semi-detached 3.0 1.12 2.5 0.35

Embedded 3.6 1.20 2.5 0.32

Basic COCOMO is good for quick estimate of software costs. However it does not account for differences in hardware constraints, personnel quality and experience, use of modern tools and techniques, and so on.

Intermediate COCOMO computes software development effort as function of program size and a set of "cost drivers" that include subjective assessment of product, hardware, personnel and project attributes. This extension considers a set of four "cost drivers",each with a number of subsidiary attributes:-

* Product attributes

Required software reliability

Size of application database

Complexity of the product

* Hardware attributes

Run-time performance constraints

Memory constraints

Volatility of the virtual machine environment

Required turnabout time

* Personnel attributes

Analyst capability

Software engineering capability

Applications experience

Virtual machine experience

Programming language experience

* Project attributes

Use of software tools

Application of software engineering methods

Required development schedule

3. What is project scheduling? Explain different techniques for project scheduling.

Ans:

Project Scheduling

Project scheduling is concerned with the techniques that can be employed to manage the activities that need to be undertaken during the development of a project.

Scheduling is carried out in advance of the project commencing and involves:

• identifying the tasks that need to be carried out;

• estimating how long they will take;

• allocating resources (mainly personnel);

• scheduling when the tasks will occur.

Once the project is underway control needs to be exerted to ensure that the plan continues to represent the best prediction of what will occur in the future:

• based on what occurs during the development;

• often necessitates revision of the plan.

Effective project planning will help to ensure that the systems are delivered:

• within cost;

• within the time constraint;

• to a specific standard of quality.

Two project scheduling techniques will be presented, the Milestone Chart (or Gantt Chart) and the Activity Network.

Milestone Charts

Milestones mark significant events in the life of a project, usually critical activities which must be achieved on time to avoid delay in the project.

Milestones should be truly significant and be reasonable in terms of deadlines (avoid using intermediate stages).

Examples include:

• installation of equipment;

• completion of phases;

• file conversion;

• cutover to the new system

Gantt Charts

A Gantt chart is a horizontal bar or line chart which will commonly include the following features:

• activities identified on the left hand side;

• time scale is drawn on the top (or bottom) of the chart;

• a horizontal open oblong or a line is drawn against each activity indicating estimated duration;

• dependencies between activities are shown;

• at a review point the oblongs are shaded to represent the actual time spent (an alternative is to represent actual and estimated by 2 separate lines);

• a vertical cursor (such as a transparent ruler) placed at the review point makes it possible to establish activities which are behind or ahead of schedule.

Activity Networks

The foundation of the approach came from the Special Projects Office of the US Navy in 1958. It developed a technique for evaluating the performance of large development projects, which became known as PERT - Project Evaluation and Review Technique. Other variations of the same approach are known as the critical path method (CPM) or critical path analysis (CPA).

The heart of any PERT chart is a network of tasks needed to complete a project, showing the order in which the tasks need to be completed and the dependencies between them.

This is represented graphically:

EXAMPLE OF ACTIVITY NETWORK

The diagram consists of a number of circles, representing events within the development lifecycle, such as the start or completion of a task, and lines, which represent the tasks themselves. Each task is additionally labelled by its time duration. Thus the task between events 4 & 5 is planned to take 3 time units. The primary benefit is the identification of the critical path.

The critical path = total time for activities on this path is greater than any other path through the network (delay in any task on the critical path leads to a delay in the project).

Tasks on the critical path therefore need to be monitored carefully.

The technique can be broken down into 3 stages:

1. Planning:

• identify tasks and estimate duration of times;

• arrange in feasible sequence;

• draw diagram.

2. Scheduling:

• establish timetable of start and finish times.

3. Analysis:

establish float;
evaluate and revise as necessary.

4. Explain the Mathematics in software development? Explain its preliminaries also.

Ans:

Mathematics in Software Development :

Mathematics has many useful properties for the developers of large systems. One of its most useful properties is that it is capable of succinctly and exactly describing a physical situation, an object or the outcome of an action. Ideally, the software engineer should be in the same position as the applied mathematician. A mathematical specification of a system should be presented, and a solution developed in terms of a software architecture that implements the specification should be produced. Another advantage of using mathematics in the software process is that it provides a smooth transition between software engineering activities. Not only functional specifications but also system designs can be expressed in mathematics, and of course, the program code is a mathematical notation – albeit a rather long-winded one.

The major property of mathematics is that it supports abstraction and is an excellent medium for modeling. As it is an exact medium there is little possibility of ambiguity: Specifications can be mathematically validated for contradictions and incompleteness, and vagueness disappears completely.

In addition, mathematics can be used to represent levels of abstraction in a system specification in an organized way. Mathematics is an ideal tool for modeling. It enables the bare bones of a specification to be exhibited and helps the analyst and system specifier to validate a specification for functionality without intrusion of such issues as response time, design directives, implementation directives, and project constraints. It also helps the designer, because the system design specification exhibits the properties of a model, providing only sufficient details to enable the task in hand to be carried out. Finally, mathematics provides a high level of validation when it is used as a software development medium. It is possible to use a mathematical proof to demonstrate that a design matches a specification and that some program code is a correct reflection of a design. This is preferable to current practice, where often little effort is put into early validation and where much of the checking of a software system occurs during system and acceptance testing.

Mathematical Preliminaries

To apply formal methods effectively, a software engineer must have a working knowledge of the mathematical notation associated with sets and sequences and the logical notation used in predicate calculus. The intent of the section is to provide a brief introduction. For a more detailed discussion the reader is urged to examine books dedicated to these subjects

Sets and Constructive Specification

A set is a collection of objects or elements and is used as a cornerstone of formal methods. The elements contained within a set are unique (i.e., no duplicates are allowed). Sets with a small number of elements are written within curly brackets (braces) with the elements separated by commas. For example, the set {C++, Pascal, Ada, COBOL, Java} contains the names of five programming languages. The order in which the elements appear within a set is immaterial. The number of items in a set is known as its cardinality. The # operator returns a set's cardinality. For example, the expression #{A, B, C, D} = 4 implies that the cardinality operator has been applied to the set shown with a result indicating the number of items in the set. There are two ways of defining a set. A set may be defined by enumerating its elements (this is the way in which the sets just noted have been defined). The second approach is to create a constructive set specification. The general form of the members of a set is specified using a Boolean expression. Constructive set specification is preferable to enumeration because it enables a succinct definition of large sets. It also explicitly defines the rule that was used in constructing the set. Consider the following constructive specification example: {n : _ | n < 3 . n} This specification has three components, a signature, n : _, a predicate n < 3, and a term, n. The signature specifies the range of values that will be considered when forming the set, the predicate (a Boolean expression) defines how the set is to be constricted, and, finally, the term gives the general form of the item of the set. In the example above, _ stands for the natural numbers; therefore, natural numbers are to be considered. The predicate indicates that only natural numbers less than 3 are to be included; and the term specifies that each element of the set will be of the form n.

Therefore, this specification defines the set {0, 1, 2} When the form of the elements of a set is obvious, the term can be omitted. For example, the preceding set could be specified as (n : _ | n < 3} All the sets that have been described here have elements that are single items. Sets can also be made from elements that are pairs, triples, and so on. For example, the set specification {x, y : _ | x + y = 10 . (x, y2)} describes the set of pairs of natural numbers that have the form (x, y2) and where the sum of x and y is 10. This is the set { (1, 81), (2, 64), (3, 49), . . .} Obviously, a constructive set specification required to represent some component of computer software can be considerably more complex than those noted here. How ever the basic form and structure remains the same.

5. What is debugging? Explain the basic steps in debugging?

Ans:

Debugging is a methodical process of finding and reducing the number of bugs, or defects, in a computer program or a piece of electronic hardware, thus making it behave as expected. Debugging tends to be harder when various subsystems are tightly coupled, as changes in one may cause bugs to emerge in another. Many books have been written about debugging (see below: Further reading), as it involves numerous aspects, including: interactive debugging, control flow, integration testing, log files, monitoring (application, system), memory dumps, profiling, Statistical Process Control, and special design tactics to improve detection while simplifying changes.

Step 1. Identify the error.

This is an obvious step but a tricky one, sometimes a bad identification of an error can cause lots of wasted developing time, is usual that production errors reported by users are hard to be interpreted and sometimes the information we are getting from them is misleading.

A few tips to make sure you identify correctly the bug are.

See the error. This is easy if you spot the error, but not if it comes from a user, in that case see if you can get the user to send you a few screen captures or even use remote connection to see the error by yourself.

Reproduce the error. You never should say that an error has been fixed if you were not able to reproduce it.

Understand what the expected behavior should be. In complex applications could be hard to tell what should be the expected behavior of an error, but that knowledge is basic to be able to fix the problem, so we will have to talk with the product owner, check documentation… to find this information

Validate the identification. Confirm with the responsible of the application that the error is actually an error and that the expected behavior is correct. The validation can also lead to situations where is not necessary or not worth it to fix the error.

Step 2. Find the error.

Once we have an error correctly identified, is time to go through the code to find the exact spot where the error is located, at this stage we are not interested in understanding the big picture for the error, we are just focused on finding it. A few techniques that may help to find an error are:

Logging. It can be to the console, file… It should help you to trace the error in the code.

Debugging. Debugging in the most technical sense of the word, meaning turning on whatever the debugger you are using and stepping through the code.

Removing code. I discovered this method a year ago when we were trying to fix a very challenging bug. We had an application which a few seconds after performing an action was causing the system to crash but only on some computers and not always but only from time to time, when debugging, everything seemed to work as expected, and when the machine was crashing it happened with many different patterns, we were completely lost, and then it occurred to us the removing code approach. It worked more or less like this:

We took out half of the code from the action causing the machine to crash, and we executed it hundreds of times, and the application crashed, we did the same with the other half of the code and the application didn’t crash, so we knew the error was on the first half, we kept splitting the code until we found that the error was on a third party function we were using, so we just decided to rewrite it by ourselves.

Step 3. Analyze the error.

This is a critical step, use a bottom-up approach from the place the error was found and analyze the code so you can see the big picture of the error, analyzing a bug has two main goals: to check that around that error there aren’t any other errors to be found (the iceberg metaphor), and to make sure what are the risks of entering any collateral damage in the fix.

Step 4. Prove your analysis

This is a straight forward step, after analyzing the original bug you may have come with a few more errors that may appear on the application, this step it’s all about writing automated tests for these areas (is better to use a test framework as any from the xUnit family).

Once you have your tests, you can run them and you should see all them failing, that proves that your analysis is right.

Step 5. Cover lateral damage.

At this stage you are almost ready to start coding the fix, but you have to cover your ass before you change the code, so you create or gather (if already created) all the unit tests for the code which is around where you will do the changes so that you will be sure after completing the modification that you won’t have break anything else. If you run this unit tests, they all should pass.

Step 6. Fix the error.

That’s it, finally you can fix the error!

Step 7. Validate the solution.

Run all the test scripts and check that they all pass.

6. What is a fish bone diagram? How is it helpful to the project management?

Ans:

FISH BONE DIAGRAM

Ishikawa diagrams (also called fishbone diagrams, cause-and-effect diagrams or Fishikawa) are causal diagrams that show the causes of a certain event -- created by Kaoru Ishikawa (1990).[1] Common uses of the Ishikawa diagram are product design and quality defect prevention, to identify potential factors causing an overall effect. Each cause or reason for imperfection is a source of variation. Causes are usually grouped into major categories to identify these sources of variation. The categories typically include:

People: Anyone involved with the process

Methods: How the process is performed and the specific requirements for doing it, such as policies, procedures, rules, regulations and laws

Machines: Any equipment, computers, tools etc. required to accomplish the job

Materials: Raw materials, parts, pens, paper, etc. used to produce the final product

Measurements: Data generated from the process that are used to evaluate its quality

Environment: The conditions, such as location, time, temperature, and culture in which the process operates

Ishikawa diagrams were proposed by Ishikawa [2] in the 1960s, who pioneered quality management processes in the Kawasaki shipyards, and in the process became one of the founding fathers of modern management.

It was first used in the 1940s, and is considered one of the seven basic tools of quality control.[3] It is known as a fishbone diagram because of its shape, similar to the side view of a fish skeleton.

Mazda Motors famously used an Ishikawa diagram in the development of the Miata sports car, where the required result was "Jinba Ittai" or "Horse and Rider as One". The main causes included such aspects as "touch" and "braking" with the lesser causes including highly granular factors such as "50/50 weight distribution" and "able to rest elbow on top of driver's door". Every factor identified in the diagram was included in the final design.

Causes

Causes in the diagram are often categorized, such as to the 8 M's, described below. Cause-and-effect diagrams can reveal key relationships among various variables, and the possible causes provide additional insight into process behavior.

Causes can be derived from brainstorming sessions. These groups can then be labeled as categories of the fishbone. They will typically be one of the traditional categories mentioned above but may be something unique to the application in a specific case. Causes can be traced back to root causes with the 5 Whys technique.

Typical categories are:

The 8 Ms (used in manufacturing)

The 8 Ps (used in service industry)

The 4 Ss (used in service industry)

One may find it helpful to use the Fishbone diagram in the following cases:

To analyze and find the root cause of a complicated problem

When there are many possible causes for a problem

If the traditional way of approaching the problem (trial and error, trying all possible causes, and so on) is very time consuming

The problem is very complicated and the project team cannot identify the root cause

When not to use it

Of course, the Fishbone diagram isn't applicable to every situation. Here are a just a few cases in which you should not use the Fishbone diagram because the diagrams either are not relevant or do not produce the expected results:

The problem is simple or is already known.

The team size is too small for brainstorming.

There is a communication problem among the team members.

There is a time constraint; all or sufficient headcount is not available for brainstorming.

The team has experts who can fix any problem without much difficulty.

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Wednesday, 5 December 2012

MC0084 solved assignment

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