Project Management (Chapter 3)

Management Spectrum

The 4 P’s

• People — the most important element of a successful project
• Senior managers - define business issues
• Project (technical) managers - control practitioners
• Practitioners - design and build systems
• Customers - set software requirements
• End-users - interact with delivered software
• Product — the software to be built
• Context -  how it fits into larger system
• Information Objectives - what data comes into and goes out of the software
• Function and Peformance
• Process — the set of framework activities and software engineering tasks to get the job done
•  Software engineering paradigm used
• Project — all work required to make the product a reality

Software Projects

•  size
•  delivery deadline
•  budgets and costs
•  application domain
•  technology to be implemented
•  system constraints
•  user requirements
•  available resources

Project Management Concerns

• Product Quality
• Risk Assessment
• Measurement
• Cost Estimation
• Project Scheduling
• Customer Communication
• Staffing
• Other Resources
• Project Monitoring

Why Projects Fail

• an unrealistic deadline is established
• changing customer requirements
• an honest underestimate of effort
• predictable and/or unpredictable risks
• technical difficulties
• miscommunication among project staff
• failure in project management

Software Teams

• the difficulty of the problem to be solved
• the size of the resultant program(s) in lines of code or function points
• the time that the team will stay together (team lifetime)
• the degree to which the problem can be modularized
• the required quality and reliability of the system to be built
• the rigidity of the delivery date
• the degree of sociability (communication) required for the project

Organizational Paradigms

• closed paradigm — structures a team along a traditional hierarchy of authority
• random paradigm — structures a team loosely and depends on individual initiative of the team members
• open paradigm — attempts to structure a team in a manner that achieves some of the controls associated with the closed paradigm but also much of the innovation that occurs when using the random paradigm
• synchronous paradigm — relies on the natural compartment-alization of a problem and organizes team members to work on pieces of the problem with little active communication among themselves

Defining the Problem

• establish scope — a narrative that bounds the problem
• decomposition — establishes functional partitioning

Melding Problem and Process

Typical Customer Communication Activity

Getting the Essence of a Project (W⁵HH Principle - B. Boehm)

• Why is the system being developed?
• What will be done? By when?
• Who is responsible for a function?
• Where are they organizationally located?
• How will the job be done technically and managerially?
• How much of each resource (e.g., people, software, tools, database) will be needed?

Critical Practices

• Formal risk analysis
• Top 10 risks, probability of becoming a problem, impact
• Empirical cost and schedule estimation
• Current estimated size of program
• Metrics-based project management
• Earned value tracking
• Defect tracking against quality targets
• People aware project management

Software Process and Project Metrics (Chapter 4)

Measurement & Metrics

... collecting metrics is too hard ...
... it's too time-consuming
... it's too political
... it won't prove anything ...

Anything that you need to quantify can be measured
in some way that is superior to not measuring it at all ...
 

What is a Metric?

Why do we Measure?

• To characterize
• To evaluate
• To predict
• To improve

A Good Manager Measures

Process Metrics

• majority focus on quality achieved as a consequence of a repeatable or managed process
• statistical SQA data: error categorization & analysis
• defect removal efficiency:  propagation from phase to phase
• reuse data

Project Metrics

• Effort / time per SE task
• Errors uncovered per review hour
• Scheduled vs. actual milestone dates
• Changes (number) and their characteristics
• Distribution of effort on SE tasks

Product Metrics

• focus on the quality of deliverables
• measures of analysis model
• complexity of the design
• internal algorithmic complexity
• architectural complexity
• data flow complexity
• code measures (e.g., Halstead)
• measures of process effectiveness
• e.g., defect removal efficiency

Metrics Guidelines

• Use common sense and organizational sensitivity when interpreting metrics data.
• Provide regular feedback to the individuals and teams who have worked to collect measures and metrics.
• Don’t use metrics to appraise individuals.
• Work with practitioners and teams to set clear goals and metrics that will be used to achieve them.
• Never use metrics to threaten individuals or teams.
• Metrics data that indicate a problem area should not be considered “negative.” These data are merely an indicator for process improvement.
• Don’t obsess on a single metric to the exclusion of other important metrics.

Software Measurement

Measurements

• Direct - Cost and effort applied
• LOC - lines of code
• execution speed
• defects reported over time period
• Indirect - functionality, quality, complexity, efficiency, reliability

Normalization for Metrics

• Normalized data are used to evaluate the process and the product
• Size-oriented normalization - line-of-code approach
• Function-oriented normalization - function point approach

Size-Oriented Metrics

• errors per KLOC (thousand lines of code)
• defects per KLOC
• $ per LOC
• page of documentation per KLOC
• errors per person-month
• LOC per person-month
• $ per page of documentation

Function-Oriented Metrics - Function Points

• errors per FP
• defects per FP
• $ per FP
• pages of documentation per FP
• FP per person-month

Why Use for FP Measures?

• independent of programming language
• uses readily countable characteristics of the "information domain" of the problem
• does not penalize inventive implementations that require fewer lines of code
• makes it easier to accomodate reuse and the trend toward object-oriented approaches

Computing Function Points

Sample Table for Calculating FP

• number of user inputs - input that provides distinct data
• number of user inputs - provides info to user (e.g. data items, screens, etc)
• number of user inputs - online input that creates an intermediate response
• number of files - each logical master file
• number of external interfaces - machine readable interfaces

Taking Complexity into Account

Measuring Software Quality

• Correctness — the degree to which a program operates according to specification
• Maintainability — the degree to which a program is amenable to change
• Integrity — the degree to which a program is impervious to outside attack
• Usability — the degree to which a program is easy to use

Defect Removal Efficiency

where

errors = problems found before release
defects = problems found after release

Managing Variation - Statistical Process Control

Software Project Planning (Chapter 5)

• The overall goal of project planning is to establish a pragmatic strategy for controlling, tracking, and monitoring a complex technical project.
• Probably the most time-consuming  project management activity
• Continuous activity from initial concept through to system delivery. Plans must be regularly revised as new information becomes available
• Various different types of plan may be developed to support the main software project plan that is concerned with schedule and budget

Types of project plan

Steps in Process

• Scoping — understand the problem and the work that must be done
• Estimation — how much effort? how much time?
• Risk — what can go wrong? how can we avoid it? what can we do about it?
• Schedule — how do we allocate resources along the timeline? what are the milestones?
• Control strategy — how do we control quality? how do we control change?

Understanding Scope

• understand the customers needs
• understand the business context
• understand the project boundaries
• understand the customer’s motivation
• understand the likely paths for change
• understand that ... nothing is guaranteed!

Cost Estimation

• project scope must be explicitly defined
• task and/or functional decomposition is necessary
• historical measures (metrics) are very helpful
• at least two different techniques should be used
• remember that uncertainty is inherent

Estimation Techniques

• past (similar) project experience
• conventional estimation techniques
•  task breakdown and effort estimates
•  size (e.g., FP) estimates
• tools (e.g., Checkpoint)

Functional Decomposition

Creating a Task Matrix (Obtained from “process framework”)

Conventional Methods: LOC/FP Approach

• compute LOC/FP using estimates of information domain values
• use historical effort for the project

Example: LOC Approach

Example: FP Approach

Tool-Based Estimation

• project characteristics
• calibration factors
• LOC/FP data

Empirical Estimation Models

Estimation Guidelines

• estimate using at least two techniques
• get estimates from independent sources
• avoid over-optimism, assume difficulties
• you've arrived at an estimate, sleep on it
• adjust for the people who'll be doing the job

The Make-Buy Decision

Computing Expected Cost

For example, the expected cost to build is:

expected cost_build = 0.30($380K)+0.70($450K) = $429 K

expected cost_reuse    = $382K
expected cost_buy       = $267K
expected cost_contract  = $410K