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7. Draw a PERT diagram and complete the table for the given set of tasks and dependencies.
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Node a b c d e f e d,f a,f,b a,f a Dependencies Time 10 10 10 20 8 5 Start Time 0 23 53 33 15 10 Stop Time 10 33 63 53 23 15
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Everything is on critical path; no slack times. See Fig. 4-7.
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b e d a
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Fig. 4-7.
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PERT diagram
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CHAPTER 4 Software Project Planning
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8. Estimate the cost parameters from the given set of data. Cost 3:8 Size (KLOC) 9. Estimate the cost parameters from the given set of data. Cost 4:0 Size (KLOC) +5.0 10. Calculate COCOMO e ort, TDEV, average sta ng, and productivity for an organic project that is estimated to be 39,800 lines of code. An organic project uses the application formulas. Cost 2:4 (KDSI)1:05 Cost 2:4 39:81:05 2:4 47:85 114:8 programmer-months TDEV 2:5 (PM)0:38 2:5 6:06 15:15 months Average sta ng Cost/TDEV 114:8=15:15 7:6 programmers Productivity 39,800 LOC/(114.8 PM 20 days/month) 17:3 LOC/programmerday 11. Calculate the unadjusted function points for the problem description of Problem 2.
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Type Inputs Simple Patient name Appt completed Appt purpose Comments Average Cancel appt Complex Total 13
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Outputs
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Calendar Supporting details Appt information Noti cation list Verify patient Check calendar Available appt Patient data
Daily schedule Weekly schedule
Inquires
Query by name Query by date
Files Interfaces Total
Software Metrics
5.1 Introduction
Science is based on measurement. Improving a process requires understanding of the numerical relationships. This requires measurement. Software measurement is the mapping of symbols to objects. The purpose is to quantify some attribute of the objects, for example, to measure the size of software projects. Additionally, a purpose may be to predict some other attribute that is not currently measurable, such as e ort needed to develop a software project. Not all mappings of symbols to objects are useful. An important concern is the validation of metrics. However, validation is related to the use of the metric. An example is a person s height. Height is useful for predicting the ability of a person to pass through a doorway without hitting his or her head. Just having a high correlation between a measure and an attribute is not su cient to validate a measure. For example, a person s shoe size is highly correlated to the person s height. However, shoe size is normally not acceptable as a measure of a person s height. The following are criteria for valid metrics1: 1. A metric must allow di erent entities to be distinguished. 2. A metric must obey a representation condition. 3. Each unit of the attribute must contribute an equivalent amount to the metric. 4. Di erent entities can have the same attribute value. Many times, the attribute of interest is not directly measurable. In this case, an indirect measure is used. An indirect measure involves a measure and a prediction formula. For example, density is not a direct measure. It is calculated from mass and density, which are both direct measures. In computer science, many of the
R. Harrison, S. Counsell, R. Nithi. An Evaluation of the MOOD Set of Object-oriented Software Metrics. IEEE TOSE 24:6, June 1998, 491 496.
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CHAPTER 5 Software Metrics
ilities (maintainability, readability, testability, quality, complexity, etc.) cannot be measured directly, and indirect measures for these attributes are the goal of many metrics programs. The following are criteria for valid indirect metrics: 1. 2. 3. 4. The model must be explicitly de ned. The model must be dimensionally consistent. There should be no unexpected discontinuities. Units and scale types must be correct.
Software Measurement Theory
The representational theory of measurement has been studied for over 100 years. It involves an empirical relation system, a numerical relation system, and a relation-preserving mapping between the two systems. The empirical relation system (E, R) consists of two parts:  A set of entities, E  A set of relationships, R The relationship is usually less than or equal. Note that not everything has to be related. That is, the set R may be a partial order.2 The numerical relation system (N, P) also consists of two parts:  A set of entities, N. Also called the answer set, this set is usually numbers natural numbers, integers, or reals.  A set of relations, P. This set usually already exists and is often less than or less than or equal. The relation-preserving mapping, M, maps (E, R) to (N, P). The important restriction on this mapping is called the representation condition. There are two possible representation conditions. The most restrictive version says that if two entities are related in either system, then the images (or pre-images) in the other system are related: x rel y iff M x rel M y 3 The less restrictive version says that if two entities are related in the empirical system, then the images of those two entities in the numerical system are related in the same way: M x rel M y if x rel y
A partial order is strictly de ned as an order that satis es three axioms: every element is related to itself, the relation cannot hold both ways between two elements, and transitivity. It is not required to be total. That is, not every two elements are related. x is related to y if and only if the mapping of x is related to the mapping of y.
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