Data Mining for Very Busy People
Computer
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
The business case for automated software engineering
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
On the relative value of cross-company and within-company data for defect prediction
Empirical Software Engineering
Defect prediction from static code features: current results, limitations, new approaches
Automated Software Engineering
On the value of learning from defect dense components for software defect prediction
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Advances in Engineering Software
Regularities in learning defect predictors
PROFES'10 Proceedings of the 11th international conference on Product-Focused Software Process Improvement
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Software engineering (SE) truisms capture broadly-applicable principles of software construction. The trouble with truisms is that such general principles may not apply in specific cases. This paper tests the specificity of two SE truisms: (a) increasing software process level is a desirable goal; and (b) it is best to remove errors during the early parts of a software lifecycle.Our tests are based on two well-established SE models: (1) Boehm et.al.'s COCOMO II cost estimation model; and (2) Raffo's discrete event software process model of a software project life cycle. After extensive simulations of these models, the TAR2 treatment learner was applied to find the model parameters that most improved the potential performance of the real-world systems being modelled.The case studies presented here showed that these truisms are clearly sub-optimal for certain projects since other factors proved to be far more critical. Hence, we advise against truism-based process improvement. This paper offers a general alternative framework for model-based assessment of methods to improve software quality: modelling + validation + simulation + sensitivity. That is, after recording what is known in a model, that model should be validated, explored using simulations, then summarized to find the key factors that most improve model behavior.