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The purpose of the present work is to mathematically model the reliability growth of a multi-version software system that is affected by infected patches.

The work presents a mathematical model that studies the reliability change due to the insertion of an infected patch in multi-version software. Various distribution functions have been considered to highlight the varied aspects of the model. Furthermore, weighted criteria approach has been discussed to facilitate the choice of the model.

The model presented here is able to quantify the effect of an infected patch on multi-version software. The model captures the hike in bug content due to an infected patch.

Multi-version systems have been studied widely, but the role of an infected patch has not been yet explored. The effect of an infected patch has been quantified by modeling the extra bugs generated in the system. This bug count would prove helpful in further studies for optimal resource allocation and testing effort allocation.

The purpose of this paper is to provide a mathematical framework to optimally allocate resources required for the discovery of vulnerabilities pertaining to different severity risk levels.

Different sets of optimization problems have been formulated and using the concept of dynamic programming approach, sequence of recursive functions has been constructed for the optimal allocation of resources used for discovering vulnerabilities of different severity scores. Mozilla Thunderbird web browser data set has been considered for giving the empirical evaluation by working with vulnerabilities of different severities.

As per the impact associated with a vulnerability, critical and high severity level are required to be patched promptly, and hence, a larger amount of funds have to be allocated for vulnerability discovery. Nevertheless, a low or medium risk vulnerability might also get exploited and thereby their discovery is also crucial for higher severity vulnerabilities. The current framework provides a diversified allocation of funds as per the requirement of a software manager and also aims at improving the discovery of vulnerability significantly.

The finding of this research may enable software managers to adequately assign resources in managing the discovery of vulnerabilities. It may also help in acknowledging the funds required for various bug bounty programs to cater security reporters based on the potential number of vulnerabilities present in software.

Much of the attention has been focused on the vulnerability discovery modeling and the risk associated with the security flaws. But, as far as the authors’ knowledge is concern, there is no such study that incorporates optimal allocation of resources with respect to the vulnerabilities of different severity scores. Hence, the building block of this paper contributes to future research.

The purpose of this paper is to develop a new software reliability growth model considering different fault distribution function before and after the change point.

In this paper, the authors have developed a framework to incorporate change-point in developing a hybrid software reliability growth model by considering different distribution functions before and after the change point.

Numerical illustration suggests that the proposed model gives better results in comparison to the existing models.

The existing literature on change point-based software reliability growth model assumes that the fault correction trend before and after the change is governed by the same distribution. This seems impractical as after the change in the testing environment, the trend of fault detection or correction may not follow the same trend; hence, the assumption of same distribution function may fail to predict the potential number of faults. The modelling framework assumes different distributions before and after change point in developing a software reliability growth model.

The purpose of this paper is to identify and quantify the key components of the overall cost of software development when warranty coverage is given by a developer. Also, the authors have studied the impact of imperfect debugging on the optimal release time, warranty policy and development cost which signifies that it is important for the developers to control the parameters that cause a sharp increase in cost.

An optimization problem is formulated to minimize software development cost by considering imperfect fault removal process, faults generation at a constant rate and an environmental factor to differentiate the operational phase from the testing phase. Another optimization problem under perfect debugging conditions, i.e. without error generation is constructed for comparison. These optimization models are solved in MATLAB, and their solutions provide insights to the degree of impact of imperfect debugging on the optimal policies with respect to software release time and warranty time.

A real-life fault data set of Radar System is used to study the impact of various cost factors via sensitivity analysis on release and warranty policy. If firms tend to provide warranty for a longer period of time, then they may have to bear losses due to increased debugging cost with more number of failures occurring during the warrantied time but if the warranty is not provided for sufficient time it may not act as sufficient hedge during field failures.

Every firm is fighting to remain in the competition and expand market share by offering the latest technology-based products, using innovative marketing strategies. Warranty is one such strategic tool to promote the product among masses and develop a sense of quality in the user’s mind. In this paper, the failures encountered during development and after software release are considered to model the failure process.

In the current market scenario, software upgrades and updates have proved to be very handy in improving the reliability of the software in its operational phase. Software upgrades help in reinventing working software through major changes, like functionality addition, feature enhancement, structural changes, etc. In software updates, minor changes are undertaken which help in improving software performance by fixing bugs and security issues in the current version of the software. Through the current proposal, the authors wish to highlight the economic benefits of the combined use of upgrade and update service. A cost analysis model has been proposed for the same.

The article discusses a cost analysis model highlighting the distinction between launch time and time to end the testing process. The number of bugs which have to be catered in each release has been determined which also consists of the count of latent bugs of previous version. Convolution theory has been utilized to incorporate the joint role of tester and user in bug detection into the model. The cost incurred in debugging process was determined. An optimization model was designed which considers the reliability and budget constraints while minimizing the total debugging cost. This optimization was used to determine the release time and testing stop time.

The proposal is backed by real-life software bug dataset consisting of four releases. The model was able to successfully determine the ideal software release time and the testing stop time. An increased profit is generated by releasing the software earlier and continues testing long after its release.

The work contributes positively to the field by providing an effective optimization model, which was able to determine the economic benefit of the combined use of upgrade and update service. The model can be used by management to determine their timelines and cost that will be incurred depending on their product and available resources.

The use of software is overpowering our modern society. Advancement in technology is directly proportional to an increase in user demand which further leads to an increase in the burden on software firms to develop high-quality and reliable software. To meet the demands, software firms need to upgrade existing versions. The upgrade process of software may lead to additional faults in successive versions of the software. The faults that remain undetected in the previous version are passed on to the new release. As this process is complicated and time-consuming, it is important for firms to allocate resources optimally during the testing phase of software development life cycle (SDLC). Resource allocation task becomes more challenging when the testing is carried out in a dynamic nature.

The model presented in this paper explains the methodology to estimate the testing efforts in a dynamic environment with the assumption that debugging cost corresponding to each release follows learning curve phenomenon. We have used optimal control theoretic approach to find the optimal policies and genetic algorithm to estimate the testing effort. Further, numerical illustration has been given to validate the applicability of the proposed model using a real-life software failure data set.

The paper yields several substantive insights for software managers. The study shows that estimated testing efforts as well as the faults detected for both the releases are closer to the real data set.

We have proposed a dynamic resource allocation model for multirelease of software with the objective to minimize the total testing cost using the flexible software reliability growth model (SRGM).

The ideology of this article is to study the performance concerns of SDN Controllers, with the help of developed SRGM and thereby obtain its optimal testing duration. The effect of undetected uncertainty in the parameter values have also been catered in the proposal.

These uncertainties in the parameter values are studied as the risk of not meeting desired set of requirements, whose removal causes additional cost. Considering these two constructs as attributes of MAUT, the controller's optimal testing duration is obtained.

The article focuses towards obtaining the optimal duration for which the SDN Controllers must be tested. It was observed that the inculcation of risk-attribute has provided the higher utility value as compared to any other existing scenarios.

Plenty of SRGM have been proposed in the literature which talks about the testing stop time determination problems. But, none of them have considered the impact of risk of not meeting the requirements (reliability) along with cost to obtain its testing stop time. Further, validation of the proposed model in presented with the help of two releases versions of SDN controller platform, ONOS, entitled as “Kingfisher” and “Loon” and has acquired promising results.

The purpose of this paper is to contribute to a growing body of research on information systems security, by studying open source alternatives for cloud computing. Several questions have been raised about the reliability of these promising but ambiguous offers, as the adoption of a cloud solution within an enterprise is generally accompanied by a change in the chief information officer (CIOs) role and loss of expertise.

The research uses a mixed research methodology: a first step is based on a questionnaire survey to investigate the security aspects of open source and understand the role of CIOs in the migration process. The investigation involved nearly 800 companies operating in the cloud computing sector in 16 European countries between November 2015 and January 2016. Then, this paper completes the research with a qualitative study by examining the activity of two sample companies.

Research confirms that open source cloud solutions offer a higher level of security than proprietary solutions. It is also noted that the role of CIOs is delegated to a third external actor: a transition CIO. Transition CIO is the guarantor of the strategic and security choices of small and medium enterprises.

These findings have important implications and great value to managers and cloud computing providers, in terms of formulating better cloud computing solutions. This study can also assist in increasing their understanding of the new role of CIO in the migration process to cloud computing.

This study contributes to the body of research on cloud computing. It is first of its kind with its focus on open source alternatives. Another novelty of this research is that it suggests a new conception for the CIOs role in the migration to cloud computing. Finally, the findings of this study would serve as a European market study to different companies interested in cloud computing.

The purpose of this paper is to explore new ways and lay a solid foundation to solve the problem of reliability growth analysis of major aerospace equipment with various uncertainty data through propose new concepts of general uncertainty data (GUD) and general uncertainty variable (GUV) and build the operation system of GUVs.

The characteristics of reliability growth data of major aerospace equipment and the limitations of current reliability growth models have been analyzed at first. The most commonly used uncertainty system analysis methods of probability statistics, fuzzy mathematics, grey system theory and rough set theory have been introduced. The concepts of GUD and GUV for reliability growth data analysis of major aerospace equipment are proposed. The simplified form of GUV based on the “kernel” and the degree of uncertainty of GUV is defined. Then an operation system of GUVs is built.

(1) The concept of GUD; (2) the concept of GUV; (3) The novel operation rules of GUVs with simplified form.

The method exposed in this paper can be used to integrate complex reliability growth data of major aerospace equipment. The reliability growth models based on GUV can be built for reliability growth evaluation and forecasting of major aerospace equipment in practice. The reliability evaluation example of a solid rocket motor shows that the concept and idea proposed in this paper are feasible. The research of this paper opens up a new way for the analysis of complex uncertainty data of reliability growth of major aerospace equipment. Moreover, the operation of GUVs could be extended to the case of algebraic equation, differential equation and matrix which including GUVs.

The new concepts of GUD and GUV are given for the first time. The novel operation rules of GUVs with simplified form were constructed.

With the rapid development of cloud computing, most software firms face the significant choice of whether they should change the versioning strategy of enterprise software from releasing the on-premise version to the software-as-a-service (SaaS) version. Data being generated and hosted on SaaS vendors' servers brings multiple effects. It enables customers to enjoy the flexibility of accessing data and using the software remotely, named the “portability” effect. However, on the other hand, the cumulative data resources on the cloud also provide a clear target for external attacks, leading to the concern of information security. Considering these, the authors hope to offer insights for software firms by exploring the strategy selection problem.

Taking the portability effect and security risks of the SaaS licensing model into account, the authors develop a two-period model to figure out the market segmentation and identify the feasible conditions for employing three alternative strategies. Comparative statics analyses are conducted to explore the influencing mechanism of exogenous factors on strategy selection. The authors also discuss the strategy selection in the presence of the network effect and the security loss faced by users of on-premise software.

One significant finding is that the on-premise strategy can be excluded when the potential loss from security risks is small. Under this circumstance, the dual version strategy is optimal provided that the increase of customer valuation caused by portability effect is below a threshold. Otherwise, the SaaS strategy generates the highest profit. When the potential loss from security risks turns large, the on-premise strategy, the dual version strategy and the SaaS strategy are the optimal options in order as the portability effect on customer valuation gets stronger.

Previous literature has insufficiently addressed the versioning issue of enterprise software. In this paper, the distinctive features of the SaaS model are considered, and differentiated results compared with previous work are obtained. The research results provide guidelines for software firms in deciding their product releases in the future.