Stress and motivation
Motivating and managing people is a tricky business. How then do we manage stress and keep people motivated?
Theory X and Theory Y
Donald McGregor identified two attitudes known as Theory X and Theory Y. In short, Theory X is someone that has an innate dislike of work and tends to avoid responsibility. They need coercion, direction and control. Theory Y, on the other hand, is someone that experience work as natural as rest or play and their commitment is typically in relation to the rewards associated with their achievements.
Each theory has a corresponding managerial approach. Theory X managers are autocratic and demand total control, managing through fear and punishment. Theory Y managers set the goal and let the person or team get on with it. With that being said, McGregor found that staff behaviour and motivation is influenced by expectations set by the manager, i.e., if the manager assumes that you are going to produce work of excellence, you are likely to try and meet it.
Models of motivation
Taylorist model
Frederick Taylor believed that people are only motivated by money. This is not always the case, even in situations where the output can be directly related to reward. Output is often constrained through norms accepted by a work group. It’s typically unspoken rules which determine the quantity of output to produce. In a software development environment it is difficult to determine and quantify the amount of work completed by an individual – it’s a team effort. Setting an excessive financial reward between employees must be considered very carefully as it can break down morale and productivity. Bonuses are usually awarded at the end of project as a work-around to this problem.
Maslow’s hierarchy of needs
Motivation is a little more complicated than Frederick Taylor puts forward. Maslow suggests that as one motivator is satisfied, another will emerge. If you are broke, money is a fairly good motivator. When the financial need is satisfied, a higher level need will emerge. The needs hierarchy starts from the basic need of food, shelter and personal safety, to the highest level which is the feeling of fulfilling your potential or self-actualization. At different stages of life people are motivated by different things. For example, an older employee consider the quality of the job more important than a younger employee. The younger employee considers a raise in pay more important than quality of job.
Herzberg’s two factor theory
Herzberg found that there are two factors in a job that will affect your satisfaction. The first is hygiene or maintenance factors. If working conditions are not right, it will definitely affect your level of satisfaction with the job. The other is motivators. These are typically a sense of achievement, or that the job is worthwhile.
Expectancy theory of motivation
Victor Vroom created the expectancy theory, which identifies three influences on motivation. They are expectancy, which is the belief that the harder you work the higher your performance will be; instrumentality, which is the belief that better performance will be rewarded, and finally the perceived value of that reward. If all three motivating factors are high, then motivation will be high. A zero score for any one of the three can remove motivation.
Oldham-Hackman job characteristic model
According the Oldham-Hackman model, there are five factors that influence job satisfaction. The first is the number of skills that can be exercised while doing the job; second is task identity, which refers to the degree to which your work identifies you as the author and lastly, the degree of influence that your work has on other people i.e., the significance of your task. These three factors determines how meaningful your job is to you. The other factors include the amount of say in the way you do your job and the feedback you receive about the work you’ve done.
There are a few simple things one can do to improve motivation, viz.,
- Set specific goals;
- Feedback on work completed;
- Tailoring tasks;
- Increase the task scope;
- Increase responsibility.
Stress
With deadlines, objectives and obstacles, life is pretty stressful as a programmer. On the other hand, some pressure is good as boredom is soul-destroying. That being said, too much pressure affects not only work quality but health too. Some major factors that cause stress is not knowing what is expected from you and what you’re responsible for. Also, being torn between responsibilities such as attending an important meeting or looking after a sick child. Research shows that productivity and quality suffers when more than 40-hours per week is exceeded. One of the axioms of extreme programming is to work no more than 40-hours per week. In a US study it was shown that people under 45 years of age who work more than 48-hours per week had double the risk of death from coronary heart disease.
To prevent overtime, project managers can be more realistic about the effort and time needed to complete a task or project. Exercising good planning and control will help to reduce unexpected obstacles. Some managers use bullying tactics and claim to be successful at pushing projects through. They typically need to create crisis to be able to justify such tactics. On the other hand, you find professional project managers that use rational, orderly and careful methods to deliver successful projects.
References
- Hughes, B. & Cotterell, M. 2009. Software Project Management, 5e. Berkshire: McGraw-Hill Education.
Project scheduling techniques
As part of planning a software project, it is always important to pay attention to the activities and the order in which they execute. This is known as a project schedule.
To create a project schedule, we have to model the project’s activities and their dependencies. Or, if you prefer, their relationships. A project schedule is typically drawn as a graph — arrows joining circles. The fancy way saying this is, edges for the arrows (a.k.a links) and vertices for the circles (a.k.a nodes). This type of graph is typically drawn from left to right, with the project’s starting date on the left.
So, what do the circles and arrows mean? Well, it depends.
Activity-on-node
This is a simple graph where the circles represent the activities and the arrows the dependencies. It is also known as a precedence network. We call it a precedence network because a node, or activity, cannot be executed until all of its preceding activities executed.
To draw an activity-on-node graph, we have to follow some rules:
- only one starting node;
- only one ending node;
- each activity has a duration;
- links have no durations;
- time moves from left to right;
- the graph may not contain any loops;
- the graph may not contain any dangles – a node that just stops in the middle of nowhere.
By the way, this activity label is based on British Standard BS 4335.
Critical Path applied to Activity-on-node
A critical path is the order in which we should execute activities, so that we can get the project done as fast as possible. Not only that, it also shows us which activities will cause us to miss the end date, if they are delayed. How do we determine the critical path?
Well, for each activity we need to have some idea of, how long it will take to do, what is the earliest we can start and complete the activity as well as the latest we can start and complete it. That is the Duration, Early Start, Early Finish, Late Start and Late Finish parts of the activity label.
To calculate the earliest dates, we need to do something called a forward pass. That is, we look at every activity and calculate its early start and early finish dates. The first activity’s early start values always starts at zero.
In the network graph above, we see that Task A and Task C can start immediately. However, Task B must wait until Task A and Task C completes (it’s predecessors). Since Task C has the longest duration (10 days) between Task A and Task C, Task B can only start in 10 days and will complete on day 13. The earliest that Task D can start is in 2 days time, after Task A completed. With this in mind, the earliest that the project can complete is in 13 days time.
Next, we need to do a backward pass on the same network to calculate the Late Start and Late Finish values. This gives us the latest date at which an activity must start and complete without delaying the end date of the project. The late finish date for the project is the same as the early finish date.
In this example, we see that the project will complete in 13 days. So, we work from the end date backwards. Starting at 13 days, we can see that Task B will take 3 days to complete and so the latest start date is 10 days (13-3). Task D also needs to finish on day 13. This task takes 4 days to complete and so the latest date to start this task is in 9 days time (13-4). Task C on the other hand must finish within 10 days and has to start immediately (10-10). Since Task A will take 2 days to complete and we know that the latest finish date is only in 9 days time, we only have to start this task on day 7 (9-2).
To find the critical path in the graph, we find the sequence of tasks that will move the end date, if there is a delay in any of its activities.
If there is a delay in Task C so that it only completes on day 11, it will delay Task B and ultimately the end date. We have to pay special attention to the critical path throughout the project so that we can handle any delays as soon as possible. The critical path sets the activity span. That is, the shortest time in which we can complete the project. If we want to shorten the time of the project, we have to cut the time of the activities in the critical path.
Activity-on-arrow
In activity-on-arrow networks, the arrows, or links, represent the activities. The nodes represent events of activities starting or finishing. Just like the activity-on-node graph, so too activity-on-arrow graphs have rules when it comes to drawing the network graph. They are:
- only one starting node;
- only one ending node;
- duration is on the link;
- nodes have no duration;
- time moves from left to right;
- nodes are numbered sequentially;
- graph may not contain any loops;
- graph may not contain any dangles.
Critical Path applied to Activity-on-arrow
Before we can find the critical path in an Activity-on-arrow graph, we need to do a forward pass. It follows the same principles as activity-on-node. The only exception is that in activity-on-arrow we use the events and not the activity start and end dates.
Let’s look at an example (Hughes and Cotterell, 2009):
The earliest date when an event can start is the date when all the events that it depends on is complete. In the example above, activities A, B and F can start immediately. So, the earliest date for event 1 is zero. Since activity A takes 6 weeks to complete, we can only start activity 2 in 6 weeks time at the earliest. Activity B takes 4 weeks to complete and so event 3 can only be achieved in week 4. Activity F is dependent on the ending date of activity E and so we only know that activity F will complete in 10 weeks. Activity E can start in 4 weeks time and will take 3 weeks to complete. From that we know that activity E will only end in 7 weeks time. We then take the longer of the two ending dates (between E and F) and find that event 5 will only be achieved in 10 weeks time. Event 4 can only start in week 9 (6 + 3) > (4 + 4). We now see that the project will only finish at the end of week 13, at the earliest.
Next, we do a backward pass on the same graph to calculate the latest date at which each event should be achieved. It follows the same principles as the backward pass for activity-on-node.
To find the critical path, we make use of slack. Slack is the difference between the earliest date and latest date. It tells us how late an event can be without affecting the end date of the project. The critical path is that path with all the nodes having a zero slack.
References
- Hughes, B. & Cotterell, M. 2009. Software Project Management, 5e. Berkshire: McGraw-Hill Education.
Estimating Software Effort
Estimating the effort it takes to produce a software product is a fairly difficult process. There are a couple of reasons why. They range from management politics to subjective guesses of how long programming tasks will take.
How then do you estimate the effort of a project with such uncertainties?
At the start of the project we should be able to get some idea of what the end-product will look like. From that, we can start estimating how long the bits and pieces will take to develop. Now that seems easy, but estimating how long the bits and pieces will take is not as simple as it seems.
Over- and under-estimating effort
Let’s look at Parkinson’s Law. It says that “Work expands so as to fill the time available for its completion.” If the task ends up being easy, then we will waste time and work less hard.
On the other hand, under-estimating effort will result in an unreliable and poor quality system. This is a manifestation of Weinberg’s Zeroth Law of Unreliability which says that “If a system doesn’t have to be reliable, it can meet any other objective.” Many people will make this sacrifice simply to complete the product before the deadline.
When a project starts falling behind, project managers will typically add more people to the project. Brook’s Law explains that “Putting more people on a late job makes it later.” Why is that?
Let’s look at an example. In a team of three members we have 3 “communication channels”. Add two more people and we have a team of five. This means that we now have 10 “communication channels” between people. We calculate it using [n * (n - 1)] / 2 where n is the number of people. Frederick P. Brooks covers this in his book The Mythical Man-Month
Estimation techniques
In his book Software Engineering Economics
Bottom-up breaks the project into it’s components and those components into it’s components and so on. Take each of the components and estimate the lines of code and multiply this with some factor that adds fat for complexity. Based on that number, calculate the number of days it will take using a ratio between lines of code and effort. [It is called bottom-up because the effort accumulates upward.]
Top-down is based on two parameters, size and productivity. Effort is then calculated as effort = size * productivity. Where size is an estimate of the number of lines of code and productivity is the time spent by the developer doing the work. The productivity parameter is scaled according the developers experience. There is a more advanced calculation. It uses the least squares regression model which is calculated as effort = constant1 + (size * constant2).
Expert judgement relies on the knowledge and experience of someone who is already involved in the project. This estimation doesn’t only rely on the person but also takes into account similar projects and supplemented by the bottom-up approach.
Case-based reasoning finds the differences and similarities between completed projects, or source cases, with the new project, the target case. Take the similarities and differences and adjust the source cases so that you get an estimate for the target case. There’s a fancy way of doing this by using the Euclidean distance between the cases. This technique is also called estimation by analogy.
Function point analysis assigns a complexity value to each instance for each of the following components, which is then summed to get the function point processing size:
- External input types – inputs that change the internal data;
- External output types – outputs from the system, such as reports;
- External inquiry types – inputs that point the system to information without modifying it;
- Logical internal file types – the information system’s data store;
- External interface file types – input and output exchanged by the information system.
Function points MarkII is an improvement on the original Allan Albrecht function point analysis technique. Create three weightings, one for input (Wi=0.58), one for entity types (We=1.66) and one for output (Wo=0.26). Then, multiply the weightings with the number of elements corresponding to each of the weightings and calculate the proportions of effort. [The values for the variables Wi, We and Wo have been set based on industry averages.]
COSMIC full function points is used for sizing real-time and embedded systems. Typically, these systems are made up of component layers which may communicate with each other. Assign a value to each data group and sum the counts to calculate the functional size units.
There are four data groups which are the inputs and outputs of these components:
- Entries – moves the data group into the component;
- Exits – moved the data group from the component;
- Reads – moves data from storage into the component;
- Writes – moves data to storage from the component.
COCOMO II is a constructive cost model where effort is calculated as person-months based on 152 hours and it’s size is measured in lines of code. Effort is calculated using the formula effort = c(size) ^ k where the constants c and k are dependent on the nature of the product and the development environment:
- Organic mode (small system developed in-house) [c=2.4, k=1 .05];
- Embedded mode (tight constraints, expensive to change) [c=3.0, k=1.12];
- Semi-detached mode (hybrid of organic and embedded) [c=3.6, k=1.20].
The way in which we calculate the effort is dependent on where we are in the development process. During application composition (user interface design) we use the number of physical features of the product such as screens, reports and so on. This is known as object points.
At the early design stage (architecture) we use function points to estimate the size of the system. There is a neat little trick here to convert the function points to the equivalent number of lines of code. To do that, we multiply the function points by a factor for the programming language used.
After we have gathered all the data we can now calculate the effort in person-months using the formula pm = 2.94(size) ^ (sf) * (e1) *…* (en)† where size is the number of lines of code in thousands (kdsi) and sf is the scale factor which is calculated by sf = 0.91 + 0.01 * Σ(exponent driver ratings)†. Exponent driver ratings are there so that we can compensate for the loss of productivity on large projects.
Determine the scale factor (sf) by assigning points from the table below to each of the following exponent drivers:
- Precedentedness - how novel is the system? The more novel, the more uncertainty, the higher the exponent;
- Development flexibility – how easy is it to meet the requirements? If it’s tough assign a higher exponent value;
- Architecture/risk resolution – how likely are the requirements to change? Very likely, up the exponent;
- Team cohesion – are your team members friends? If not, up the exponent;
- Process maturity – do you know what you are doing? If you do, go low on the exponent.
Here’s a table to help you out on the exponent driver values:
†Pssst… I changed the formula’s a little. There are variables which have been set for many years and so the formulas should really be pm = A(size) ^ (sf) * (e1)*…*(en) and sf = B + 0.01 * Σ(exponent driver ratings).
References
- Hughes, B. & Cotterell, M. 2009. Software Project Management, 5e. Berkshire: McGraw-Hill Education.
leave a comment