These are metrics that pertain to Product Quality. These metrics measure the impact of organizational economics, employee satisfaction, communication, and organizational growth factors of the project.
These metrics enable management to understand the quality of the software, the productivity of the development team, code complexity, customer satisfaction, agile process, and operational metrics.
Schedule Variance: Any difference between the scheduled completion of an activity and the actual completion is known as Schedule Variance. Effort Variance: Difference between the planned outlined effort and the effort required to actually undertake the task is called Effort variance. Requirement Stability Index: Provides visibility to the magnitude and impact of requirements changes.
Productivity Project : Is a measure of output from a related process for a unit of input. Schedule variance for a phase : The deviation between planned and actual schedules for the phases within a project. Effort variance for a phase : The deviation between a planned and actual effort for various phases within the project. Cost of quality : It is a measure of the performance of quality initiatives in an organization. Cost of poor quality : It is the cost of implementing imperfect processes and products.
In addition to testing, it tracks the defects at all phases of the development cycle, including the design reviews, code inspections, and formal verifications before testing.
Because a large percentage of programming defects is related to design problems, conducting formal reviews, or functional verifications to enhance the defect removal capability of the process at the front-end reduces error in the software. The pattern of phase-based defect removal reflects the overall defect removal ability of the development process.
With regard to the metrics for the design and coding phases, in addition to defect rates, many development organizations use metrics such as inspection coverage and inspection effort for in-process quality management. This metric can be calculated for the entire development process, for the front-end before code integration and for each phase. It is called early defect removal when used for the front-end and phase effectiveness for specific phases.
The higher the value of the metric, the more effective the development process and the fewer the defects passed to the next phase or to the field. This metric is a key concept of the defect removal model for software development.
Although much cannot be done to alter the quality of the product during this phase, following are the fixes that can be carried out to eliminate the defects as soon as possible with excellent fix quality. Fix backlog is related to the rate of defect arrivals and the rate at which fixes for reported problems become available. It is a simple count of reported problems that remain at the end of each month or each week.
Using it in the format of a trend chart, this metric can provide meaningful information for managing the maintenance process. If BMI is larger than , it means the backlog is reduced. If BMI is less than , then the backlog increased.
The fix response time metric is usually calculated as the mean time of all problems from open to close. Short fix response time leads to customer satisfaction. The important elements of fix responsiveness are customer expectations, the agreed-to fix time, and the ability to meet one's commitment to the customer. Fix quality or the number of defective fixes is another important quality metric for the maintenance phase.
A fix is defective if it did not fix the reported problem, or if it fixed the original problem but injected a new defect. For mission-critical software, defective fixes are detrimental to customer satisfaction. The metric of percent defective fixes is the percentage of all fixes in a time interval that is defective. A defective fix can be recorded in two ways: Record it in the month it was discovered or record it in the month the fix was delivered. The first is a customer measure; the second is a process measure.
Consistent in its use of units and dimensions. The mathematical computation of the metric should use measures that do not lead to bizarre combinations of unit. Programming language independent. Metrics should be based on the analysis model, the design model, or the structure of the program itself. An effective mechanism for quality feedback. That is, the metric should provide a software engineer with information that can lead to a higher quality end product.
Ans: It tells us how does an organization combine metrics that come from different individuals or projects. Ans : Normalization approaches:. Pages of documentation per KLOC. Are dependent on the programming language. Penalize well-designed but short programs.
Cannot easily accommodate non-procedural languages.
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