No abstract

Software Reliability Growth Models (SRGMs) are supporting software industries in expecting and scrutinizing quality of software. Numerous SRGMs have been proposed; majority of which concentrate on testing period of software. For testing, domain specific knowledge plays a very crucial role. Based on necessity condition, a set of programmes are in testing phase of software development. “Domain testing is a software technique in which small number of test cases is selected for trial. These sets of testing paths, all of which are to be eventually influenced by designed test cases are called the testing domain which expands with the progress of testing”. Keeping this concept in mind, we propose SRGMs with the concept of testing domain with exponential coverage. Utility of proposed framework has been emphasized in this paper through some models pertaining to different distribution i.e Exponential, Logistic, Weibull and Rayleigh. Moreover, the data analysis is performed to find the estimates of parameters by fitting the models on authentic data sets.

Recent advances in the software world have seen the rise of various Software Reliability Growth Models (SRGMs). These SRGMs take into account various factors and associate them to reliability to come out with a new approach. Some of them consider calendar time as the governing factor while others argued that effort based modeling is more towards reality. In the real industrial scenario, due to the ever growing demands of the customer and stiff contention in the market, developers generally prefer to release the software with multiple versions instead of rolling out all the functionalities at one go. Consequently the customary approach towards software development process as observed in practice is iterative in nature. Moreover, the debugging process is not a perfect event. It has bottlenecks owing to the increasing complexity of software due to up gradations which transcends the limited knowledge of testing team. Thus, it is not a practical approach to go with the assumption of fault removal with certainty after the failure is observed. In a real scenario, it may happen that initially the testing team may not be skilled enough to detect all the faults leading to imperfect debugging, also during debugging it may happen that some faults are added fault causing error generation. Some models successfully capture the influence of imperfect debugging on multiple releases of software. In this present paper, we present a two stage detection/correction based software reliability growth model with testing effort, integrating the concept of two types of imperfect debugging in multiple up gradations of a software. We have taken Exponential and logistic distribution functions for detection and correction process in this paper. The proposed model is successfully tested on a real life software data set.

No abstract

Software testing is an important phase of the software development life cycle to achieve highly reliable software. Due to the time and resource limitation during the testing phase, firms do not attempt to deliver a complete and perfect product in one development cycle. They plan multi upgradations of software by adding new functionalities. Many models have been developed in the past which discuss about when to stop testing and when to release the software to the users. But they have been limited to the study of single version only. In the present framework, we describe a unified approach to address an important issue of when to stop testing the multi-upgradation of software, which is a complex process. The total debugging cost for each upgradation includes the cost of debugging in the warranty period along with the testing cost. It is assumed that the software is supported till the warranty period is over. In the proposed cost model for each upgradation, we consider that some of the remaining faults of previous release are reported and removed partly during the testing period and partly during the warranty period of new upgradation. An algorithm for finding the optimal release time for each version is developed. We estimate the parameters of the model using Statistical Package for Social Sciences on the real data set, and obtain optimum stopping time for each version of the software using Maple software.