Estimating Size for Object-Oriented Software

In this paper, a metric is proposed, Predictive Object Points (POPs), that has the potential to do all of the above. Unlike traditional measures, POPs are based on an object oriented paradigm, encapsulating object behavior and the interaction between objects. The POPs measure combines several contemporary metrics to establish an overall measure suitable for predicting effort and/or tracking productivity. The metric at the heart of the POP calculation is Weighted Methods per Class (WMC). WMC looks at each top level class (or each distinct object from the user’s perspective) and assigns a weight to the behaviors of that class that are seen by the world. The “weight” of an object’s behavior is determined by evaluating the effects that the behavior has on the objects in the system (by counting the properties that this behavior impacts) and the amount of control the objects in the system have over this behavior (by counting the parameters of the method or the pieces of information that get passed to it). The calculated WMC metric is combined with information about the groupings of objects into classes and the relationships between these classes of objects to arrive at a value which appears to correlate to the effort associated with implementing a solution. Because the metric is based on behavior, it mimics much of what is useful about a Function Point measure. Since behavior is something widely understood, a POP value can be determined by someone with only a little knowledge of implementation details. And, since the things that impact behavior “weight” are well understood, detailed counting rules can be established for the POP metric

Comparison Study and Review on Object-Oriented Metrics

The best elucidations to software development problems are regularly touted as object-oriented processes. The popularity of object-oriented design metrics is essential in software engineering for measuring the software complexity, estimating size, quality and project efforts. There are various approaches through which we can find the software cost estimation and predicates on various kinds of deliverable items. Object-oriented metrics assures to reduce cost and the maintenance effort by serving as early predictors to estimate software faults. Such an early quantification augments the quality of the final software. This paper reviews object-oriented metrics. A comparison table is maintained via which we can analyze the difference between all the object-oriented metrics effectively

Review of complexity metrics for object oriented software products.

It is widely accepted that sizing or predicting the volumes of various kinds of software deliverable items is one of the first and most dominant aspects of software cost estimating. Most of the cost estimation model or techniques usually assume that software size or structural complexity is the integral factor that influences software development effort. Although sizing and complexity measure is a very critical due to the need of reliable size estimates in the utilization of existing software project cost estimation models and complex problem for software cost estimating, advances in sizing technology over the past 30 years have been impressive. This paper attempts to review the 12 object-oriented software metrics proposed in 90s’ by Chidamber, Kemerer and L

A systematic review of software development cost estimation studies

This paper aims to provide a basis for the improvement of software estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) Increase the breadth of the search for relevant studies, 2) Search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) Conduct more studies on estimation methods commonly used by the software industry, and, 4) Increase the awareness of how properties of the data sets impact the results when evaluating estimation methods