Software Reliability Growth and Innovation Diffusion Models: An Interface

Kapur, P. K.; Singh, Ompal; Mittal, Rubina

doi:10.1142/s0218539304001567

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…1. This expectation is consistent with the diffusion behavior theorized in research on information systems development (Kwon and Zmud 1987) and quality (Kapur et al 2004), sociology (Kumar and Kumar 1992), management (Chatterjee and Eliasberg 1990) and marketing (Mahajan et al 1990). …”

Section: Learning Maturitysupporting

confidence: 88%

Managing the diffusion of organizational learning behavior

2008

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This paper addresses the need for organizations to manage the transformation from traditional hierarchical models to 'learning organizations. ' We propose a fivestage methodology useful in the diffusion of behaviors associated with organizational learning (OL) theory. The stages of OL diffusion are (1) agenda-setting, (2) matching, (3) restructuring, (4) clarifying, and (5) routinizing. Each stage involves both managerial (structural) or member (cultural) influences on organizational memory (OM). Salient definitions are provided and the OM aspects and deliverables associated with each OL diffusion stage are discussed. This research provides a theoretically-driven approach to help change agents diffuse and realize the potential of OL behavior in the firm.Organizational learning (OL) has been accepted as a useful theory in information systems (IS) research for many years. Among IS studies using the theory, OL has been seen as a way to resolve conflict resulting from disruptive technologies (Sherif et al. 2006), a software diffusion theory (Fichman and Kemerer 1997), a possible explanation for the productivity paradox (see Arthur and Huntley 2005), a predictor of organizational competitive advantage (Bhatt and Grover 2005), and a way to alleviate barriers to effective change (Templeton and Snyder 1999). The significance of the theory in IS research naturally motivates the question: How can managers diffuse behavior in organizational units so that the many promising benefits of OL may materialize? Pursuant to this question, our research uses innovation diffusion theory to develop a methodology managers can use to guide the adoption of OL behavior in the firm.Building a 'learning organization' is considered an ongoing challenge in organizational development (Kiernan 1993). This is the first attempt to articulate a theoretically driven guide managers can use to diffuse OL behavior into the firm. It is our intention to provide a framework that will facilitate and possibly encourage organizational development efforts so that OL will diffuse more rapidly throughout modern organizations. Based on classical innovation diffusion theory pioneered by Everett Rogers (1983), we propose a two-phased OL diffusion approach. The first phase is initiation and includes two major stages: agenda-setting and matching. The second phase is implementation and involves three stages: restructuring, clarifying and routinizing. Thus, the approach uses two phases comprised of five steps. In describing our method, we illuminate salient issues managers may encounter during each of five prescribed stages for OL diffusion. There are at least two major contributions of the paper: (1) we contribute to Rogers' IDT theory by being the first to use it as a lens for viewing OL implementation and (2) we contribute to OL theory by providing a rare discussion of OL diffusion. Managers following this method can reduce the risks associated with the transformation to a learning organization and enhance ongoing learning effectiveness.Inf Syst Front (

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Section: Learning Maturitysupporting

confidence: 88%

Managing the diffusion of organizational learning behavior

2008

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“…Studies in innovation diffusion have been based on ideas taken from various and distinct fields (Aggrawal, 2014). Researchers have been using the studies on fault removal phenomenon from software reliability engineering and it has helped to understand the modeling in the innovation diffusion process (Aggrawal, 2014;Anand, 2013;Kapur et al, 2004b). And so the two diversified field have joined in an effort to better understand the real life problems in more line and depth.…”

Section: Introductionmentioning

confidence: 99%

Unified approach for modeling innovation adoption and optimal model selection for the diffusion process

Anand

Agarwal

Aggrawal

et al. 2016

Journal of Advances in Management Research

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Purpose – Mathematical modeling of innovation diffusion is a constantly evolving field within marketing science. The diffusion process explains the dispersion of an innovation among potential buyers. Prior research on innovation diffusion has been based on modeling varied aspects of real life situations in marketing. One such aspect is studying the adoption process depending on the awareness and motivation level among the customers. Awareness is having knowledge of an innovation, whereas motivation is about the perception of an individual. In line with these aspects, the purpose of this paper is to propose a unified modeling framework for the adoption process based on the awareness and motivation about the product. Design/methodology/approach – When the market is well informed about the product, there are some people who are motivated and some, who have adopted the product earlier and shall now influence others in their buying behavior. It is very much similar to queuing system in which some units are waiting in a queue for the service, service for some units are being processed and some units have already been served. This analogous behavior between two approaches has motivated the use of infinite server queuing theory in modeling adoption of the product. Thereafter, the authors have proposed a unification scheme to model different market scenarios. Findings – From analyzing the values of comparison criteria, it was not clear that which among them is performing best. Thus there was a need for an approach which can judiciously find the optimal model. For this very purpose the authors applied distance-based approach which was capable of computing the optimal model based on the distance of attribute value from the optimal. The analysis performed on two real life sales data sets depict that model in which awareness is following logistic pattern and motivation and adoption are following a constant pattern is ranked one. Research limitations/implications – The idea has been validated on product. It would be interesting to know how the methodology works on service. Originality/value – The modeling framework discussed in this paper can be helpful to know from the available set of alternative, which among them is performing better in capturing the spread of the product in the market. The proposed framework offer some managerial guidance by highlighting the unusual aspects of diffusion process and also present an approach to judge the best among a set of different models.

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“…This is because of the presence of fault dependencies and similarities, where removing one fault can reveal and remove other faults as well. This highlights the importance of thorough debugging and testing to identify and remove all faults, including those that may not be immediately apparent but could cause future failures if left unaddressed [9].…”

Section: Introductionmentioning

confidence: 99%

Using Modified Diffusion Models for Reliability Estimation of Open Source Software

Chen¹,

Huang

2023

IEEE Access

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Software development is a highly unpredictable process, and ensuring software quality and reliability before releasing it to the market is crucial. One of the common practices during software development is the reuse of code. It can be achieved by utilizing libraries, frameworks, and other reusable components. Practically, when a fault is detected in replicated code, developers must check for similar faults in other copies, as there is a dependency between faults. To prevent recurrence of observed failures, developers must remove the corresponding leading fault and any related dependent faults. Many software reliability growth models (SRGMs) have been proposed and studied in the past, but most SRGMs assume that developers usually detect only one fault causing a failure. In actuality, it is necessary to consider the possibility of detecting multiple faults that may share similarities or dependencies. Additionally, some SRGMs rely on specific assumptions that may not always be valid, such as perfect debugging and/or immediate debugging. In this study, the modified diffusion models are proposed to handle these unrealistic situations, and are expected to better capture the dynamics of open source software (OSS) development. Experiments using real OSS data show that the proposed models can accurately describe the fault correction process of OSS. Finally, an optimal software release policy is proposed and studied. This policy takes into account some factors, including the remaining number of faults in the software, the expenses associated with identifying and rectifying those faults, and the level of market demand for the software. By considering these factors, developers can determine the optimal time to release the software to the market.

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Software Reliability Growth and Innovation Diffusion Models: An Interface

Cited by 9 publications

References 17 publications

Managing the diffusion of organizational learning behavior

Managing the diffusion of organizational learning behavior

Unified approach for modeling innovation adoption and optimal model selection for the diffusion process

Using Modified Diffusion Models for Reliability Estimation of Open Source Software

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