Software engineering metrics and models

TL;DR: An overview of the state of the art of software cost estimation (SCE), and what can software project management expect from SCE models, how accurate are estimations which are made using these kind of models, and what are the pros and cons of cost estimation models.

TL;DR: The paper reports experience from the development and use of eleven different software maintenance effort prediction models and suggests the use of prediction models as instruments to support the expert estimates and to analyse the impact of the maintenance variables on the maintenance process and product.

TL;DR: The results of the study indicate that when a combined third generation and fourth generation languages data set were used, the neural network produced improved performance over conventional regression analysis in terms of mean absolute percentage error.

TL;DR: A simple transformation of the metric is investigated whereby the cyclomatic complexity is divided by the size of the system in source statements, thereby determining a complexity density ratio, which is demonstrated to be a useful predictor of software maintenance productivity on a small pilot sample of maintenance projects.