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NHPP software reliability model considering the uncertainty of operating environments with imperfect debugging and testing coverage

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APPLIED MATHEMATICAL MODELLING 2017年 51卷 68-85页

作者： Li, Qiuying Pham, Hoang Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China Sci & Technol Reliabil & Environm Engn Lab Beijing Peoples R China Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ 08854 USA

In this paper, we propose a testing-coverage software reliability model that considers not only the imperfect debugging (ID) but also the uncertainty of operating environments based on a non-homogeneous Poisson process (NHPP). Software is usually tested in a given control environment, but it may be used in different operating environments by different users, which are unknown to the developers. Many NHPP software reliability growth models (SRGMs) have been developed to estimate the software reliability measures, but most of the underlying common assumptions of these models are that the operating environment is the same as the developing environment. But in fact, due to the unpredictability of the uncertainty in the operating environments for the software, environments may considerably influence the reliability and software's performance in an unpredictable way. So when a software system works in a field environment, its reliability is usually different from the theory reliability, and also from all its similar applications in other fields. In this paper, a new model is proposed with the consideration of the fault detection rate based on the testing coverage and examined to cover ID subject to the uncertainty of operating environments. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real software failure data based on seven criteria. Improved normalized criteria distance (NCD) method is also used to rank and select the best model in the context of a set of goodness-of-fit criteria taken all together. All results demonstrate that the new model can give a significant improved goodness-of-fit and predictive performance. Finally, the optimal software release time based on cost and reliability requirement and its sensitivity analysis are discussed. (C) 2017 Elsevier Inc. All rights reserved.

关键词： testing coverage Uncertainty Operating environment Imperfect debugging (ID) Software reliability growth models (SRGMs) Non-homogeneous Poisson process (NHPP)

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A testing-coverage software reliability growth model considering the randomness of the field environment 2

A testing-coverage software reliability growth model conside...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Mao Chengyong Li Qiuying Beihang Univ Sch Reliabil & Syst Engn Sci & Technol Reliabil & Environm Engn Lab Beijing 100191 Peoples R China

ISBN: (纸本)9781509037131

Many studies show that software reliability growth models (SRGMs) have good performance when considering the randomness of the field environments. In this paper, we propose a new software reliability model incorporating S-shaped testing coverage with the randomness of the field environments. Parameters of the new model are estimated by means of the least square estimation (LSE) method. Comparison of this model with other existing NHPP models has also been presented. Five goodness-of-fit criteria are used to compare the performance of the proposed model with several existing NHPP SRGMs based on one actual failure data set collected from software application. The results show that the proposed model can fit significantly better than all other existing NHPP models.

关键词： software reliability model testing coverage the randomness of the field environment

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Considering testing-coverage and fault removal efficiency subject to the random field environments with imperfect debugging in software reliability assessment 27

Considering testing-coverage and fault removal efficiency su...

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27th IEEE International Symposium on Software Reliability Engineering (ISSRE)

作者： Li, Qiuying Mao, Chengyong Beihang Univ Sch Reliabil & Syst Engn Sci & Technol Reliabil & Environm Engn Lab Beijing Peoples R China

ISBN: (纸本)9781509036011

As the quantitative software reliability assessment means, software reliability model has become one of the most successful and practical software reliability engineering contributions. Although many models have been developed in the past 30 years, most of them take the following assumptions: 1) the software's final field environment is the same as its testing environment, so the software reliability model used in testing environment will be suitable for its field applications;2) fault removal efficiency equals to 1, i.e. the faults detected will be removed perfectly and not part of the faults will remain. But these two assumptions do not always stand in practice. Firstly, software is usually tested in a given controlled environment, but it might be used in different field environments by different users, so due to the randomness of the field environments, the software's performance and reliability may be considerably influenced in an unpredictable way. Secondly, the fault removal efficiency is usually imperfect, i.e. the faults causing the observed failures may not be removed completely and the original faults may remain and new faults may be introduced during the debugging process. This paper aims to incorporate fault removal efficiency, testing coverage and the randomness of field environments into software reliability assessment and develop a new software reliability model. We compare the performance of the proposed model with several existing nonhomogeneous Poisson process (NHPP) software reliability growth models (SRGMs) using software failure data collected from real applications. All results show that the new model can give an improved goodness-of-fit.

关键词： testing coverage fault removal efficiency field environment imperfect debugging (ID) software reliability growth models

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Generative AI in the Software Development Lifecycle

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COMPUTER 2024年第12期57卷 27-34页

作者： Bannon, Tracy (Trac) Laplante, Phil MITRE Corp Bedford MA 01730 USA NIST Gaithersburg MD 20899 USA Penn State Univ Software & Syst Engn State Coll PA 16801 USA

We conducted a virtual roundtable on generative AI (GenAI), human/machine teaming, and the future of the software development lifecycle (SDLC). Experts from industry, academia, and government explored how GenAI is tra... 详细信息

关键词： Industries Generative AI Government Software Development Management Business Software Development Processing Software Business Best Practices Difficult Task Good And Bad Language Model Software Engineering Key Performance Indicators Repetitive Tasks Debugging Security Challenges Software Quality Customer Requirements testing coverage Code Generation Employee Satisfaction Psychological Safety Software Architecture

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Reliability assessment of coverage execution oriented software growth model and genetic algorithm driven release planning

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年 1-11页

作者： Kaur, Gurjeet Tandon, Abhishek Mittal, Rubina Singh, Atul Univ Delhi Dept Operat Res Delhi India Univ Delhi Keshav Mahavidyalaya Dept Math Delhi India Univ Delhi Ramjas Coll Dept Stat Delhi India

Understanding the process of execution of code is essential in many software engineering activities, such as debugging, optimization, security analysis, and comprehending complicated systems. The paper provides a thorough investigation of approaches for analyzing the execution flow of code. Software testing is a vital step in the development phase of software, in which problems are discovered to ensure that the software system is reliable before it is released to consumers. Achieving thorough test coverage is critical for improving testing effectiveness. This work gives a thorough investigation into the execution-oriented code-based testing coverage reliability growth model for software. The growth model is a useful tool for anticipating software reliability in the software testing lifecycle phase. The model incorporates different testing coverage functions grounded on three types of execution orientation of code. The model's distinctiveness is demonstrated by verifying it on a software fault dataset and then comparing the estimation findings to the Inflection S Shaped Model. The research also uses Genetic Algorithms to optimize the release planning process by repeatedly creating and assessing release plans based on execution-based coverage modeling, resulting in the best-fit release timing. The release optimization problem aims to reduce testing costs while achieving a specified percentage of fault elimination.

关键词： Software reliability growth model testing coverage Execution of code Release planning

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A Personal Retrospective on Symbolic Execution

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2025年第3期51卷 706-709页

作者： Clarke, Lori A. Univ Massachusetts Amherst Amherst MA 01003 USA

The 1976 paper describes the early development of a Symbolic Execution systems including some of the software engineering challenges, before internet communication and interactive computing were the norm. It describes the early expectations for such systems and contrasts those with the actual benefits and difficulties that arose. It also describes some of the surprising lessons learned.

关键词： Codes Indexes testing Software systems Model checking Data collection Arrays Algorithms Training Syntactics Assertion languages program verification symbolic execution test data generation testing coverage

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Software reliability prediction and release time management with coverage

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INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2022年第3期39卷 741-761页

作者： Panwar, Saurabh Kumar, Vivek Kapur, P. K. Singh, Ompal Univ Delhi Dept Operat Res New Delhi India Amity Univ Amity Ctr Interdisciplinary Res Noida India

Purpose Software testing is needed to produce extremely reliable software products. A crucial decision problem that the software developer encounters is to ascertain when to terminate the testing process and when to release the software system in the market. With the growing need to deliver quality software, the critical assessment of reliability, cost of testing and release time strategy is requisite for project managers. This study seeks to examine the reliability of the software system by proposing a generalized testing coverage-based software reliability growth model (SRGM) that incorporates the effect of testing efforts and change point. Moreover, the strategic software time-to-market policy based on costreliability criteria is suggested. Design/methodology/approach The fault detection process is modeled as a composite function of testing coverage, testing efforts and the continuation time of the testing process. Also, to assimilate factual scenarios, the current research exhibits the influence of software users refer as reporters in the fault detection process. Thus, this study models the reliability growth phenomenon by integrating the number of reporters and the number of instructions executed in the field environment. Besides, it is presumed that the managers release the software early to capture maximum market share and continue the testing process for an added period in the user environment. The multiattribute utility theory (MAUT) is applied to solve the optimization model with release time and testing termination time as two decision variables. Findings The practical applicability and performance of the proposed methodology are demonstrated through real-life software failure data. The findings of the empirical analysis have shown the superiority of the present study as compared to conventional approaches. Originality/value This study is the first attempt to assimilate testing coverage phenomenon in joint optimization of software time to market and testi

关键词： Software reliability assessment Release time decisions Reliability growth models Change point Postrelease testing testing coverage Reporters testing efforts

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Analyzing the Effects of White Noise on Software Release Planning Using SDE-Based SRGM

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FLUCTUATION AND NOISE LETTERS 2024年第2期23卷 2440003-2440003页

作者： Aggarwal, Anu G. Anand, Sameer Bibyan, Ritu Verma, Vibha Univ Delhi Dept Operat Res New Delhi 110007 India Int Sch Business & Media Bangalore 562132 India

The efficiency and performance of a software application largely depend on the testing strategy adopted by the firm. Apart from the tools, techniques, and skills used for testing, the duration also plays an important influence in establishing software reliability. This defines the operational performance of the software. The testing duration decision is dependent on the failure behavior depicted by the software during the process of testing and the cost spent at various phases of development. In this paper, we study the multi-release model whose fault removal process is affected by random irregular fluctuations (white noise) and error generation phenomenon and determine the optimal time of testing for a multi-release-based software. The fault removal process is governed by the phenomenon of testing coverage which is affected by random fluctuations. The model shows encouraging results as it handles the stochastic property of the fault detection process. The optimal testing time is determined with the goal to minimize the expected development cost related to software while achieving the desired reliability levels of software for that release using a Genetic Algorithm. A real-life four-release fault dataset of tandem computers has been used to numerically demonstrate the methodology. It is observed through sensitivity analysis that the presence of white noise directly affects the cost and optimal testing duration. The potential to improve sensitivity, flexibility, early detection, discovering unsuspected patterns, and boost fault diagnosis is enhanced by collecting irregular fluctuations in the fault detection rate. This technology, by going beyond existing methodologies, has distinct advantages for detecting faults and can contribute to more dependable and efficient systems in a variety of domains.

关键词： Software reliability growth model white noise random variation software release planning testing coverage error generation multi-release genetic algorithm stochastic differential equation

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A testing-coverage software reliability model with the uncertainty of operating environments

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International Journal of Systems Science: Operations and Logistics 2014年第4期1卷 220-227页

作者： Chang, In Hong Pham, Hoang Lee, Seung Woo Song, Kwang Yoon Department of Computer Science and Statistics Chosun University Gwangju South Korea Department of Industrial and Systems Engineering Rutgers University Piscataway NJ United States

In this paper, we develop a new testing-coverage software reliability model with the uncertainty of operating environments. Examples are included to illustrate the goodness-of-fit of proposed model and several existing non-homogenous Poisson process (NHPP) models based on four sets of failure data collected from software applications. The explicit mean value function solution for the proposed model is presented. The results show that the proposed model fits significantly better than other existing NHPP models based on three criteria such as mean squared error, predictive power and predictive ratio risk. © 2014, © 2014 Taylor & Francis.

关键词： mean squared error non-homogeneous Poisson process predictive power predictive ratio risk software reliability testing coverage

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An Efficient Method for Parameter Estimation of Software Reliability Growth Model Using Artificial Bee Colony Optimization 5th

An Efficient Method for Parameter Estimation of Software Rel...

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5th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Mallikharjuna, Rao K. Kodali, Anuradha GITAM Univ Dept IT Visakhapatnam Andhra Pradesh India GRIET Sch Comp Hyderabad Andhra Pradesh India

ISBN: (纸本)9783319202945;9783319202938

One of the established trends of research areas and practices in software engineering that dealt with the measurement and enhancement of reliability is software reliability engineering. Stochastic software reliability models find typical usage for analysis. These are the models that perform modeling on failure process of the software and exploit other software metrics or failure data as cornerstone for parameter estimation. The ability of the models for estimating and predicting the current reliability and future failure behavior, respectively, is high. Due to any failures and faults in the system, a product becomes unreliable. The lack of understanding of nature of the software makes the measurement of software reliability as a challenging task. It is not possible to determine a best way to measure the reliability and other aspects of software. This paper proposes an efficient software reliability growth model (SRGM) in which logistic exponential TEF is exploited. This model offers increased failure rate recognition and suitable ways to resolve faults and so on. Our work estimates the SRGM parameters using optimization algorithm. Such estimation can aid in developing precise software reliability model. In order to accomplish the optimization, we use artificial bee colony (ABC) algorithm. As the parameters optimization considerably improves the quality of parameters to be used for reliability growth model, reliability growth can also be improved considerably.

关键词： Software reliability Logistic exponential TEF testing termination time testing coverage Artificial bee colony Swarm intelligence Genetic algorithm

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