314. Software Fault Prediction Model for …

1 Pradeep Singh et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 2348-2354. Software Fault Prediction Model for embedded Systems: A Novel finding Pradeep Singh1, Shrish Verma2. 1,2. National Institute of Technology, Raipur, India Abstract Software testing plays a vital role in Software models [4], [5] are proposed. A panel at IEEE Metrics 2002. development especially when the Software developed is [6] concluded that manual Software reviews can find mission, safety and business critical applications.

2 Software approximately 60% of faults. Automated models proposed testing is the most time consuming and costly phase. for Fault Prediction are useful tools for Software Prediction of a modules info Fault -prone and non Fault prone prior to testing is one of the cost effective technique. Predicting organizations and significantly better in terms of Fault a safe module as faulty increases the cost of projects by more detection performance, compared to other verification , cautious and better-test resources allocation for those validation and testing activities [5][17][18].

3 These modules, whereas Prediction of faulty code as Fault free code automated models uses static code attributes such as Lines end up in under-preparation and may leave modules untested of Code (LOC) and the McCabe/Halstead complexity and this may cause accidental failure and lead towards massive other Software attributes that can be easily extracted from loss . In this research, we present a novel Fault Prediction source code repositories even for large systems. technique that reduces the probability of false alarm (pf) and Software Fault Prediction uses various method-level static increases the precision for detection of faulty modules.

4 The code Software metrics such as Halstead , Mc- Cabe metrics general expectation from a predictor is to get very high probability of false alarm (pf) to get more reliable and quality etc. to categorize modules and predicting them either Fault - Software product. We have taken embedded systems Software prone(fp) or non- Fault prone( nfp) modules by using for this study and the goal is to predict as many faulty modules classification Model derived from the data of projects. In as possible. In this paper we apply a supervised discretization Software Fault Prediction problems, we have X = {x1, x2.}

5 For pre-processing and clustering based classification for xn}where x represents Software module that is characterized Prediction of a modules info Fault -prone (fp) and non Fault - by Software metrics and Y = {fp,nfp} ,where an unknown prone (nfp) modules. To evaluate this approach we perform an system S predictive Model transforms from instances X to extensive comparative experimental study for the effectiveness predicted classes Y; Y = S(X). Prediction models based on of our method with benchmark results for the same embedded Software metrics, can estimate number of faults in Software Software 's.

6 Our Fault Prediction Model produces better results than the standard and benchmark approaches for Software modules as well as which module is faulty. So the Fault Prediction . predictive models are easy to use and faster to run for Results from our proposed Model significantly decreases highlighting Fault -prone modules compared to inspections probability of false alarm (pf) down to 9% while increasing [4],[5],[7]. Fault predictors models are useful tools for precision and balance rates at 68% and 79% respectively.

7 Software organizations to manage their testing resources effectively through focusing on Fault -prone Software Keywords Software Fault Prediction , supervised discretization, modules to mprove Software quality. Many researchers and Software metric. have already worked for Fault Prediction Model and various Software metrics and techniques like linear regression, I. INTRODUCTION decision trees, neural networks and Naive Bayes The demand of highly reliable and secure system is classification have been analyzed [5][8][9].

8 Increasing day by day. To fulfill these demands by the ever- This study specifically includes projects of white goods increasing power of computing devices, systems are manufacturer from the Turkish Software industry for growing complex. Due to the complexity of these systems, embedded systems domain. embedded systems are used in effort and cost incur in development is increasing. Software many industries such as white goods, automotive, complexity is the main source of failure and potential telecommunications and aerospace [10].

9 We produce the hazards. High quality Software within allocate budget experiments on these embedded system Software 's with requires a careful planning and cost effective use of testing cluster based classification in order to compare it by Ayse resources. Mission critical or safety and business critical et al.[23] framework for the same embedded systems. We system needs more reliability and therefore requires more use the same pd, pf, precisions and balance in order to testing time and resources. analyze the effectiveness of our framework.

10 Our results Testing phase is the most expensive, time and resource indicate that cluster based classification used after consuming phase of the Software development lifecycle supervised discretization process may increase the requires approximately 50% of the whole project schedule Prediction performance significantly. We implemented [1, 2]. So an effective and intelligent test strategy can high-performance Fault Prediction Model based on minimize the time of testing by using resources efficiently.