Predictive Technique in Software Engineering
A Predictive Technique in Software Engineering is a Software Engineering Task that deals with designing Predictive Models.
- AKA: Predictive Software Model.
- Example(s):
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- Counter-Example(s):
- See: Software Development, Requirement Analysis, Computer Programming, Systems Analysis, Applied Computer Science.
References
2017
- (Shirabad, 2017) ⇒ Jelber Sayyad Shirabad. (2017). "Predicitve Techniques in Software Engineering" In: (Sammut & Webb, 2017) DOI:10.1007/978-1-4899-7687-1_661
- QUOTE: Software engineering (SE) is a knowledge- and decision-intensive activity. From the initial stages of the software life cycle (i.e., requirement analysis), to the later stage of testing the system, and finally maintaining the software through its operational life, decisions need to be made which impact both its success and failure. For instance, during project planning one needs to be able to forecast or predict the required resources to build the system. At the later stages such as testing or maintenance it is desirable to know which parts of the system may be impacted by a change, or are more risky or will require more intensive testing.
The process of developing software can potentially create a large amount of data and domain knowledge. The nature of the data, of course, depends on the phase in which the data were generated. During the requirement analysis, this data most times is manifested in the form of documentations.
- QUOTE: Software engineering (SE) is a knowledge- and decision-intensive activity. From the initial stages of the software life cycle (i.e., requirement analysis), to the later stage of testing the system, and finally maintaining the software through its operational life, decisions need to be made which impact both its success and failure. For instance, during project planning one needs to be able to forecast or predict the required resources to build the system. At the later stages such as testing or maintenance it is desirable to know which parts of the system may be impacted by a change, or are more risky or will require more intensive testing.