Measuring software reliability

Kumar, Ravinder; Khatter, Kiran; Kalia, Arvind

doi:10.1145/2047414.2047425

Cited by 9 publications

(9 citation statements)

References 10 publications

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“…Incomplete information on the software structure, principles and functioning, heterogeneity of its composition, presence of imported elements, and insufficient specifications make it difficult to evaluate and predict the software reliability. In these cases, traditional approaches to acquisition and forecasting of reliable values are associated with significant costs; that is why models based on the fuzzy sets of theory that allow estimating the software reliability with practically acceptable accuracy are of immediate interest [26][27][28].…”

Section: Fuzzy Model Of Software Reliability and Security Evaluation-mentioning

confidence: 99%

Models for Testing Modifiable Systems

Markov¹,

Barabanov²,

Tsirlov³

2018

Probabilistic Modeling in System Engineering

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The work describes reliability and security growth models for modifiable software systems as a result of revisions and tests performed for specified input data areas. The work shows that the known reliability growth models are of monotonically increasing type, which is not in line with current multi-version team technologies of software development that are primarily based on the open-source code. The authors suggest new nonmonotonically increasing models of software reliability evaluation and planning that allow taking into account the effect of decreased reliability resulting from updates or wavefront errors. The work describes the elaborated bigeminal and generic reliability evaluation model as well as the models and test planning procedures. The work includes calculated expressions for the evaluation of the model accuracy and shows that the developed models are adequate to real data. An example is given of transition from probability models to fuzzy models in case of incomplete basic data. The work provides general recommendations for selection of software tool testing models.

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Section: Fuzzy Model Of Software Reliability and Security Evaluation-mentioning

confidence: 99%

Models for Testing Modifiable Systems

Markov¹,

Barabanov²,

Tsirlov³

2018

Probabilistic Modeling in System Engineering

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“…Then these values are processed by inference engine in the fuzzy domain using the rule base created by the expert. Finally the processed output is transformed from fuzzy domain to crisp domain by defuzzification module [10]. Following sub-sections briefly describe the working of each of the module of fuzzy system.…”

Section: Fuzzy Modelmentioning

confidence: 99%

Usability Evaluation of Human–Biometric–Sensor Interaction using Fuzzy Model

Kumar¹

2017

IJARCSSE

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Abstract-Usability measure user preferences and performance of interaction with a system. Criteria for usability evaluation vary system to system depending upon the selection among wide variety of usability parameters. To enhance the acceptability of biometric based identification among large population usability evaluation of HumanBiometrics Sensor Interaction (HBSI) is highly important. This paper proposed a fuzzy based usability evaluation method for HBSI. Effectiveness, Efficiency and user satisfaction are considered the most accurate one for taking quick decision of usability analysis of HBSI. Each of the attribute is better judged by linguistic values as compared to numerical values. Therefore, these attributes are best describes by fuzzy aspects. This paper develops a fuzzy model for usability evaluation of application software using these attributes.

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“…In [13], presented a fuzzy model for software reliability prediction. Authors proposed three parameters i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

An Approach for Analyzing ISO / IEC 25010 Product Quality Requirements based on Fuzzy Logic and Likert Scale for Decision Support Systems

Iqbal¹,

Babar²

2016

ijacsa

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Abstract-Decision Support Systems (DSS) are collaborative software systems that are built to support controlling of an organization in decision making process when faced with nonroutine problems in a specific application domain. It's important to measure portability, maintainability, security, reliability, functional suitability, performance efficiency, compatibility, and usability quality requirements of DSS properly. ISO / IEC 25010 which replaced ISO 9126, used for three different quality models for software products, such as: a) Quality in use model, b) Product quality model, and c) Data quality model. There is a lack of methodologies to measure and quantify these quality requirements. Fuzzy logic used to specify quality requirements of DSS, because it's an approach to computing based on degrees of truth, rather than true or false logics. Likert scale is a method in which it converts qualitative values into quantitative values to make a best statistical analysis. The measurement and quantification of quality requirements of DSS is a challenging task, because these quality requirements are in qualitative form and can't be represented in quantitative way. Although, several quality requirements methods for DSS have been proposed so far, but the research on analyzing quality requirements of DSS are still limited. In this paper, quantitative approach proposed for analyzing ISO / IEC 25010 product quality requirements based on fuzzy logic and likert scale for DSS which aims to quantify quality requirements. Moreover implemented proposed framework on a case study 'Internet Banking' and got data from 25 respondents i.e. System Analysts and Domain Experts of banking sector.

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Measuring software reliability

Cited by 9 publications

References 10 publications

Models for Testing Modifiable Systems

Models for Testing Modifiable Systems

Usability Evaluation of Human–Biometric–Sensor Interaction using Fuzzy Model

An Approach for Analyzing ISO / IEC 25010 Product Quality Requirements based on Fuzzy Logic and Likert Scale for Decision Support Systems

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