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Software Project Management




                    Notes          plan adequately any forthcoming activities. As far as estimation and prediction is concerned
                                   there is still a number of unsolved problems and errors. To obtain good results it is necessary to
                                   take into consideration  any previous  projects. Estimating  the effort  with a high  grade  of
                                   dependability is a problem which has not yet been solved and even the project manager has to
                                   deal with it since the beginning.


                                          Example: The estimation of the effort invested in the development of software projects
                                   can turn into a complicated problem to be solved if the appropriate models are not available.
                                   Unfortunately, until this moment this is the situation, since there are not the necessary records
                                   in the software development companies. Years of investigation are required in order to obtain
                                   the volumes of information needed to carry out a prediction with a good level of reliability and
                                   with a low error margin.
                                   The domains are not the most suitable, due to their size and limited number of variables, and
                                   because of the fact that they depend on the particular casuistry of each company. The quality of
                                   the prediction can improve if more appropriate sets of data are available and more deep study
                                   of the methods is performed.
                                   Sets of data are provided bellow. Each set shows information about certain amount of software
                                   development projects. For each project, there are two variables: one, (independent variable) that
                                   refers to the size of the generated code -measured in lines of code or function points-, and the
                                   other (dependant variable) that indicates the effort (time) invested in the development of projects.
                                   Columns “Size” and “Effort” show the measure used. Column “Projects” shows the number of
                                   projects in the data.


                                      Data      Projects              Size                       Effort
                                      Set 1       21         function points                  person-days
                                      Set 3       18         thousands of lines of code       man-months
                                      Set 4       33         lines of code                    man-months
                                      Set 6       35         lines of code                    person-hours
                                      Set 11      33         function points                  man-months


                                   Effort Estimation is briefly explained in Unit-6.

                                   2.6 Risk Identification

                                   Risk identification is utilized in risk management to answer the questions: What can happen?
                                   How can it happen? Risk identification is the process of distinguishing the opportunities opened
                                   up by each activity or phase of the project and clarifying where the risk lies.  The agreed lenience
                                   of risk should help identify the amount of time should be spent in identifying risk, but, at least
                                   the 20% of the risks that would have 80% of the potential impact should be identified.  There are
                                   many techniques to aid in risk identification and they generally fall under the heading of either
                                   quantitative or qualitative risk identification techniques.

                                   2.6.1 Qualitative Risk Identification Techniques

                                   Following are the major qualitative techniques of Risk Identification:
                                      Assumptions Analysis

                                      Check Lists





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