scholarly journals Determining Software Time-to-Market and Testing Stop Time when Release Time is a Change-Point

Author(s):  
Ompal Singh ◽  
Saurabh Panwar ◽  
P. K. Kapur

In software engineering literature, numerous software reliability growth models have been designed to evaluate and predict the reliability of the software products and to measure the optimal time-to-market of the software systems. Most existing studies on software release time assessment assumes that when software is released, its testing process is terminated. In practice, however, the testing team releases the software product first and continues the testing process for an added period in the operational phase. Therefore, in this study, a coherent reliability growth model is developed to predict the expected reliability of the software product. The debugging process is considered imperfect as new faults can be introduced into the software during each fault removal. The proposed model assumes that the fault observation rate of the testing team modifies after the software release. The release time of the software is therefore regarded as the change-point. It has been established that the veracity of the performance of the growth models escalates by incorporating the change-point theory. A unified approach is utilized to model the debugging process wherein both testers and users simultaneously identify the faults in the post-release testing phase. A joint optimization problem is formulated based on the two decision criteria: cost and reliability. In order to assimilate the manager’s preferences over these two criteria, a multi-criteria decision-making technique known as multi-attribute utility theory is employed. A numerical illustration is further presented by using actual data sets from the software project to determine the optimal software time-to-market and testing termination time.

Author(s):  
SHINJI INOUE ◽  
YUKI NAKAGAWA ◽  
SHIGERU YAMADA

Software failure-occurrence or the fault-detection phenomenon is changed notably in an actual testing phase or an operational phase due to the changes of factors influencing the software reliability growth process. Taking into consideration of the effect of the change in software reliability growth modeling is expected to conduct more accurate software reliability assessment because it is said that such approach enables us to conduct more plausible software reliability assessment reflecting an actual testing-environment. We develop a framework for developing software hazard rate models with effect of change-point, and discuss change-point detection methods for applying our model to quantitative software reliability assessment. Additionally, we discuss an optimal software release problem for estimating optimal shipping time with the effect of the change as one of the application problems of our model in software project management. Finally, we show numerical examples for software reliability assessment and our optimal software release policy based on our change-point model by using actual data.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rama Rao Narvaneni ◽  
K. Suresh Babu

PurposeSoftware reliability growth models (SRGMs) are used to assess and predict reliability of a software system. Many of these models are effective in predicting future failures unless the software evolves.Design/methodology/approachThis objective of this paper is to identify the best path for rectifying the BFT (bug fixing time) and BFR (bug fixing rate). Moreover, the flexible software project has been examined while materializing the BFR. To enhance the BFR, the traceability of bug is lessened by the version tag virtue in every software deliverable component. The release time of software build is optimized with the utilization of mathematical optimization mechanisms like ‘software reliability growth’ and ‘non-homogeneous Poisson process methods.’FindingsIn current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.Originality/valueIn current market scenario, this is most essential. The automation and variation of build is also resolved in this contribution. Here, the software, which is developed, is free from the bugs or defects and enhances the quality of software by increasing the BFR.


Author(s):  
Tadashi Dohi ◽  
Naoto Kaio ◽  
Shunji Osaki

This paper presents a new stochastic model for determining the optimal release time for a computer software in testing phase, taking account of the debugging time lag. In the earlier works, most of software release models were considered, but it was assumed that an error detected can be removed instantaneously. In other words, none discussed quantitatively the effect of the software maintenance action in the optimal software release time. Main purpose of this work is to relate the optimal software release policy with the arrival-service process on the software operation phase by users. We use the Non-Homogeneous Poisson Process (NHPP) type of software reliability growth models as the software error detection phenomena and obtain the optimal software release policies minimizing the expected total software costs. As a result, the usage circumstance of a software in operation phase gives a monotone effect to the software release planning.


2014 ◽  
Vol 490-491 ◽  
pp. 1267-1278 ◽  
Author(s):  
Tean Quay Lee ◽  
Chun Wu Yeh ◽  
Chih Chiang Fang

Software Reliability Growth Models (SRGMs) provide techniques to predict future failure behavior from known characteristics of the software testing work. However, in some cases, software developers did not have sufficient historical data to estimate the corresponding reliability and the expected testing cost, especially for a newly developed software project, and thus the results obtained from analytical models may not be reliable. In such situations, Bayesian analysis is a reasonable approach to additionally take expert's opinions into account for better decision making. In this paper, we utilized Yamada Delayed S-shaped Model with Bayesian analysis in predicting software reliability and expected testing costs to determine an optimal release time for software systems. Besides, the failure process of software are assumed to be drawn from a non-homogeneous Poisson process (NHPP), and the parameters of the proposed model are assumed to be mutually independent and Gamma distributed. Finally, a numerical example is given to verify the effectiveness of the proposed approach, and the sensitive and risk analyses are performed in light of the numerical example.


2008 ◽  
Vol 4 (4) ◽  
pp. 124-128
Author(s):  
Vikram Singh

Deciding when to stop testing and deliver or release a software product in the competitive market place, is an important decision in software project management. Software tools are available in the market for backing the decisions of project managers with regards to software release. Also, software development houses may need to know “for how long should they support and maintain their software product after release?” A few tools are available that take into account the software product support activities that go beyond software release while computing and optimizing software life cycle cost. A simulation based approach has been devised for helping Software Project Managers in deciding: 1) How long to test software? 2) How long to provide free support to the product? 3) When to withdraw the product support?


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