Assessing reliability of modular software

Kubat, Peter

doi:10.1016/0167-6377(89)90031-x

Cited by 89 publications

(38 citation statements)

References 7 publications

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“…Two most popular estimation techniques are Maximum Likelihood Estimation (MLE), and Least Squares Estimation (LSE) [1], [2], [26]. For example, using the method of LSE, the evaluation formula of (5) with is depicted as Minimize: (11) where is the cumulative testing-effort actually consumed in time , and is the cumulative TE estimated by (5). Differentiating with respect to , , and , setting the partial derivatives to zero, and rearranging these terms, we can solve this type of nonlinear least square problems.…”

Section: B Methods Of Model's Parameter Estimationmentioning

confidence: 99%

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Optimal Testing Resource Allocation, and Sensitivity Analysis in Software Development

Huang

Lyu

2005

IEEE Trans. Rel.

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Abstract-We consider two kinds of software testing-resource allocation problems. The first problem is to minimize the number of remaining faults given a fixed amount of testing-effort, and a reliability objective. The second problem is to minimize the amount of testing-effort given the number of remaining faults, and a reliability objective. We have proposed several strategies for module testing to help software project managers solve these problems, and make the best decisions. We provide several systematic solutions based on a nonhomogeneous Poisson process model, allowing systematic allocation of a specified amount of testing-resource expenditures for each software module under some constraints. We describe several numerical examples on the optimal testing-resource allocation problems to show applications & impacts of the proposed strategies during module testing. Experimental results indicate the advantages of the approaches we proposed in guiding software engineers & project managers toward best testing resource allocation in practice. Finally, an extensive sensitivity analysis is presented to investigate the effects of various principal parameters on the optimization problem of testing-resource allocation. The results can help us know which parameters have the most significant influence, and the changes of optimal testing-effort expenditures affected by the variations of fault detection rate & expected initial faults.

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Section: B Methods Of Model's Parameter Estimationmentioning

confidence: 99%

“…However, the above may have some negative components if , making infeasible for (28) & (29). If this is the case, the solution can be corrected by the following steps [4], [5], [10]:…”

Section: A Minimizing the Number Of Remaining Faults With A Given Fimentioning

confidence: 99%

Optimal Testing Resource Allocation, and Sensitivity Analysis in Software Development

Huang

Lyu

2005

IEEE Trans. Rel.

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“…Interactions have a certain probability of happening, and are associated with a transition probability. This view follows the Kubat model [19], who expresses the software architecture as a Discrete-Time Markov Chain (DTMC), where vertices represent the execution of software components, and arcs enumerate the probability of transferring the execution flow to the next component. Software interactions are specified for each link from component c i to c j , which are annotated with the probability that component c j is executed after component c i , denoted as p ij .…”

Section: Component Deploymentmentioning

confidence: 99%

Fitness Landscape Characterisation for Constrained Software Architecture Optimisation Problems

Aleti

Moser

2015

2015 20th International Conference on Engineering of Complex Computer Systems (ICECCS)

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Abstract-The automation of software architecture design is an important goal in software engineering. A plethora of automated design exploration techniques have been devised in the last decades to handle the complexity of making design decision in large scale, complex software systems. The common aim of these methods is the optimisation of quality attributes, such as reliability and safety. The majority of approaches use heuristic methods, such as local search or genetic algorithms, which use gradients in the fitness space to guide the search to the local optimum. When problems are constrained, search gradients are disrupted by infeasible regions, which may have a great impact on the difficulty of solving optimisation problems.Discovering the conditions under which a search heuristic will succeed or fail is critical for understanding the strengths and weaknesses of different software architecture optimisation methods. This paper investigates how to adequately characterize the features of constrained problem instances that have impact on difficulty in terms of algorithmic performance, and how such features can be defined and measured for the component deployment optimisation problem. We employ fitness landscape characterisation metrics that measure uniformity of the gradients in the search space, and investigate how two different constraints shape the search space, and as a result affect the performance of software architecture optimisation approaches.

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“…Two different approaches are used for assessing reliability of software architecture. They are path based [26,35,45] and state based [14,27,28]. SPE [36], WS [43], PASA [42], CM [16], BIM [7], ABI [4], AABI [2] are the approaches for predicting performance at the architectural level.…”

Section: 2mathematical Model Based Software Architecture Evaluationmentioning

confidence: 99%

Software Architecture Evaluation Methods A Survey

Shanmugapriya¹,

Suresh²

2012

IJCA

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Software architectural evaluation becomes a familiar practice in software engineering community for developing quality software. Architectural evaluation reduces software development effort and costs, and enhances the quality of the software by verifying the addressability of quality requirements and identifying potential risks and it provides assurance to developers that their chosen architecture will meet both functional and non-functional quality requirements. This paper presents a discussion on different software architectural evaluation methods and techniques and concentrates on summarizing the importance of the different early and late evaluation methods, similarities and difference between them, their applicability, strengths and weaknesses.

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Assessing reliability of modular software

Cited by 89 publications

References 7 publications

Optimal Testing Resource Allocation, and Sensitivity Analysis in Software Development

Optimal Testing Resource Allocation, and Sensitivity Analysis in Software Development

Fitness Landscape Characterisation for Constrained Software Architecture Optimisation Problems

Software Architecture Evaluation Methods A Survey

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