Optimal allocation of testing effort during testing and debugging phases: a control theoretic approach

Kapur, P. K.; Pham, Hoang; Chanda, Udayan; Kumar, Vijay

doi:10.1080/00207721.2012.669861

Cited by 45 publications

(25 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…to solve the dynamic optimization problems. In order to solve the above control problem, we start with the Hamiltonian (Sethi & Thompson, 2005;Kapur et al, 2012;2013) which can be written as…”

Section: Optimal Solutionmentioning

confidence: 99%

“…Kapur et al (2013) discussed the problem to minimize the cost and maximize the reliability during the testing phase. Kapur et al (2013) used exponential shaped SRGM and proposed a technique to find the optimal time to release of software. Kapur et al (2010Kapur et al ( , 2013 did not mention the concept of time lag during the modeling.…”

Section: Introductionmentioning

confidence: 99%

“…In the first approach authors selected several variations of one original SRGM for obtaining the self-combination model and secondly they have selected several various candidate SRGMs for obtaining the multi-combinational model. Kapur et al (2010) proposed a model to allocate the resources and minimize the testing cost during the testing phase under dynamic condition. Kapur et al (2013) discussed the problem to minimize the cost and maximize the reliability during the testing phase.…”

Section: Introductionmentioning

confidence: 99%

“…Kapur et al (2010) proposed a model to allocate the resources and minimize the testing cost during the testing phase under dynamic condition. Kapur et al (2013) discussed the problem to minimize the cost and maximize the reliability during the testing phase. Kapur et al (2013) used exponential shaped SRGM and proposed a technique to find the optimal time to release of software.…”

Section: Introductionmentioning

confidence: 99%

“…Kapur et al (2013) used exponential shaped SRGM and proposed a technique to find the optimal time to release of software. Kapur et al (2010Kapur et al ( , 2013 did not mention the concept of time lag during the modeling. Kumar et al (2014) proposed a resource allocation model for detection and correction purpose with fixed budgetary constraint with the assumption that detection and correction are two concurrent activities.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An effort allocation model considering different budgetary constraint on fault detection process and fault correction process

Kumar¹,

Sahni²

2016

10.5267/j.dsl

Self Cite

View full text Add to dashboard Cite

Fault detection process (FDP) and Fault correction process (FCP) are important phases of software development life cycle (SDLC). It is essential for software to undergo a testing phase, during which faults are detected and corrected. The main goal of this article is to allocate the testing resources in an optimal manner to minimize the cost during testing phase using FDP and FCP under dynamic environment. In this paper, we first assume there is a time lag between fault detection and fault correction. Thus, removal of a fault is performed after a fault is detected. In addition, detection process and correction process are taken to be independent simultaneous activities with different budgetary constraints. A structured optimal policy based on optimal control theory is proposed for software managers to optimize the allocation of the limited resources with the reliability criteria. Furthermore, release policy for the proposed model is also discussed. Numerical example is given in support of the theoretical results.Growing Science Ltd. All rights reserved. 6

show abstract

Section: Optimal Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An effort allocation model considering different budgetary constraint on fault detection process and fault correction process

Kumar¹,

Sahni²

2016

10.5267/j.dsl

Self Cite

View full text Add to dashboard Cite

show abstract

Automatic cloud service testing and bottleneck detection system with scaling recommendation

Liu

Sheu

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

Performance problems in a cloud service are difficult to diagnose because they may be caused by various system components. This study proposes an automatic cloud service testing and bottleneck detection system that is applicable to different types of services. With the proposed test module, the user can customize test scenarios to automatically test and collect the corresponding metrics of the target service. Afterward, the proposed bottleneck detection algorithm analyzes the collected metrics and determines whether a bottleneck is presented in the target system. The bottleneck detection module also provides a scaling recommendation for the service provider to facilitate the service system reconfiguration. The experimental results reveal that the proposed system could detect a potential bottleneck in a service system accurately. In accordance with the scaling recommendation, the performance of the target cloud service can be improved efficiently after reconfiguration. Therefore, usage of the proposed system can ensure a high quality of service, and the service level objective could be fulfilled.

show abstract

Selection of optimal software reliability growth models using an integrated entropy–Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach

Kumar

Saxena

Garg

2021

Math Methods in App Sciences

View full text Add to dashboard Cite

A large number of software reliability growth models (SRGMs) have been studied to estimate the reliability of software systems over the past 40 years. Different models have been developed upon different sets of assumptions. A few models were developed in a practical environment by considering testing effort, testing coverage, time delay fault correction, and fault reduction factor. Generally, SRGMs are not dataset independent, and thus the selection of an appropriate SRGM for use in a specific application is a challenging task in the software reliability area. The wrong selection of SRGMs might create a wrong estimation of reliability and consequently a delay in the release of the software. To overcome this problem, we have proposed a unique hybrid entropy weight‐based multi‐criteria decision‐making (MCDM) method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach for the selection of a suitable SRGM and applied it for optimal selection and ranking of SRGMs. The proposed hybrid approach identifies the need of the relative importance of criteria for a given application without which inter‐criterion comparison cannot be accomplished. It requires a set of model selection criteria along with a set of SRGMs and their level of criteria for optimal selection. It successfully displays the result in terms of a merit value which is used to rank the SRGMs. The proposed approach has been validated on two data sets of the real software failure. The results of the study play a vital role for the decision maker to judge the suitability of the SRGM.

show abstract

Optimal allocation of testing effort during testing and debugging phases: a control theoretic approach

Cited by 45 publications

References 26 publications

An effort allocation model considering different budgetary constraint on fault detection process and fault correction process

An effort allocation model considering different budgetary constraint on fault detection process and fault correction process

Automatic cloud service testing and bottleneck detection system with scaling recommendation

Selection of optimal software reliability growth models using an integrated entropy–Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach

Contact Info

Product

Resources

About