Can any someone help me in finding the names of the software quality estimation models we can use during the development of software to control its quality (like minimizing the number of errors and finding error prone modules)?
* Software Quality Estimation using Machine Learning:
Software quality estimation is one of the most interesting research areas in the domain of software engineering for last few decades. Large numbers of techniques and models have already been worked out in the area of error estimation. The aim of software quality estimation is to identify error prone tasks as the cost can be minimized with advance knowledge about the errors and this early treatment of error will enhance the software quality. In this paper we have explored a set of data in university setting. This paper advocates the use of case-based reasoning (i. e. , CBR) to make a software quality estimation system by the help of human experts. CBR relies on historical information from similar past projects, whereby similarities are determined by comparing the projects, and key attributes.
* Software Quality Modeling and Estimation with Missing Data :
Software quality estimation models generally exploit the software engineering measurements hypothesis that software metrics encapsulate the underlying quality of the software system. A typical model is trained using software measurements and fault data of a similar, previously developed project. Such a strategy requires complete knowledge of fault data for all of the training modules. However, various practical software engineering issues limit the availability of fault data for all modules in the training data. We present a semi-supervised learning scheme as a solution to software defect modeling when there is limited prior knowledge of software quality. The commonly used EM algorithm for estimating missing data values is used in conjunction with k-means clustering.
* Quality Estimation (Software Metrics)
A software metrics is a quantitative measure to explain at what degree an attribute of testing or product quality or process has performed. It uses more than one measure to provide a measurable information.
10 Steps for a Successful Metrics
1. Identify clear and measurable goals
2 . Define the granularity of measurements and drill down the key variables.
3 . Ask questions and select metrics.
4 . Decide on periodicity of metrics.
5 . Establish a measurement method.
6 . Define the reporting mechanisms.
7 . Generate hypotheses around key variables /factors.
8 . Collect both quantitative and qualitative data.
9 . Analyze metrics and take action items.
10. Track action items and ensure the improvement based on the metrics result.
* Estimating Software Quality with Advanced Data Mining Techniques
Independent variables include various software metrics as McCabe, Error Count, Halstead, Line of Code, etc... In this paper we present the use of advanced tool for data mining called Multimethod on the case of building software fault prediction model. Multimethod combines different aspects of supervised learning methods in dynamical environment and therefore can improve accuracy of generated prediction model. We demonstrate the use Multimethod tool on the real data from the Metrics Data Project Data (MDP) Repository
My topic is " improving software quality estimation model using neuro-fuzzy system" but not sure which software quality estimation model should I use.
Do check out COQUALMO (Constructive Quality Model) that was developed at University of Southern California. There are certain pre-requisites for using this model; but I am sure it will be of help.
Software Quality Estimation is an area of interest for me too. Let me know if this model meets your requirements.
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