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Using Software Metrics Burak Turhan

Outline. IntroductionHP ExperienceNASA ExperienceRelationships between concepts (i.e. SC Complexity

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Using Software Metrics Burak Turhan

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    1. Using Software Metrics Burak Turhan

    2. Outline Introduction HP Experience NASA Experience Relationships between concepts (i.e. SC Complexity & Maintanence diffculty). Conclusions

    3. Papers (IEEE Computer Special Issue on Software Metrics, 1994) R.Grady, “Successfully Applying Software Metrics” D.L.Lanning and T.M.Khoshgoftaar, “Modeling the Relationship Between Source Code Complexity and Maintenance Difficulty” G.Stark, R.Durst and C.W.Vowell, “Using Metrics in Management Decision Making” C.Jones, “Software metrics”

    4. ‘Success’ is marked by a measurable end point. Software metrics are measures of success for software products and development processes. Many sophisticated tools with many possible project variables. Keep it simple. Introduction

    5. Remember the real meaning of using metrics and models. Create dialogue btw. Managers and developers or customers and suppliers. Goal of prediction is to compare the actual and predicted. Observation: Useful results are generally obtained from simple models rather than intractable and non-interpretable exact, complex models. When the results are not reasonable, actions should be taken. Be careful while using metrics. Introduction

    6. Major Uses of Software Metrics

    7. The basis for estimates ? Data collection. The bottom line for high level managers: Is the project on schedule? Schedule slip. Monitoring progress against estimates. Tracking functionality Found and fixed defects Project Estimation and Progress Monitoring

    8. Schedule Slip

    9. Tracking Functionality

    10. Found and Fixed Defects

    11. Work product: “An intermediate or final output that describes the design, operation, manufacture or test of some portion of a deliverable or salable product. It is not the final product.” Strong bias to code metrics in metric applications, due to ease of extraction. Cyclometric complexity. Fanout for data-oriented/design complexity Evaluation of Work Products

    12. NASA MOD initiated software metrics program in May 1990: Relevant to the environment. Cost-effective collection and analysis. Multiple metrics to crosscheck. Strong basis in industry practice for setting ‘rule-of-thumb’ thresholds. 3-step process to implement Definition Documentation Education Properties of Metrics

    13. Problem 1 How to tell if the project is on schedule and within budget? Earned-value charts.

    14. Problem 2 How hard will it be for another organization to maintain this software? McCabe Complexity

    15. Problem 3 How to tell when the subsystems are ready to be integrated Defect Density Metrics.

    16. How to tell if a test schedule makes sense? Historical data. Problem 4

    17. Canonical Correlation Analysis

    18. Preliminary Work

    19. Conclusions Rather than seeking for new metrics, seek for efficent use of existing hundreds of metrics. Build simple models that are easily interpretable. Analyse several types of relationships. (i.e. Linear, non-linear, observable & latent variables, combination of models in serial or parallel).

    20. Questions ?

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