How Software Quality Mediates the Impact of Intellectual Capital on Commercial Open-Source Software Company Success

Garomssa, Shimels D.; Kannan, Rathimala; Chai, Ian; Riehle, Dirk

doi:10.1109/access.2022.3170058

Cited by 5 publications

(1 citation statement)

References 80 publications

(247 reference statements)

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“…Stressing the importance of investing in maintenance activities is essential because software bugs jeopardize functionality. According to a report from the Consortium for Information and Software Quality (Garomssa et al, 2022), only in the US, poorcoded software caused a staggering 2.08 trillion dollars in losses in the year 2020 and 4.4 billion people were impacted by software flaws in 2016 alone, resulting in a 1.1 trillion-dollar loss to the global economy. The ISO/IEC 9126 standard has identified efficiency, functionality, maintainability, portability, reliability, and usability as characteristics of high-quality software (Jung et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Software Maintainability Prediction Using Multiple Linear Regression and Predictor Importance

Yadav,

Singh

2023

IJERR

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Accurate maintenance effort and cost estimation are essential for effective software development. By identifying software modules with poor maintainability, Software Maintainability Prediction (SMP) plays a crucial role in managing software maintenance expenses. Previous research efforts have used multiple regression techniques to predict software maintainability, but the results regarding various accuracy and performance metrics are inconclusive. As such, developing a methodology that can recommend regression techniques for software maintainability prediction in the face of inconsistent performance or accuracy metrics is imperative. This research addresses the critical issue of software maintainability and presents a novel approach, the Software Maintainability Model (SMP) utilizing the Predictor Importance (PI) Method, Multiple Linear Regression (MLR), and five machine learning techniques. The proposed SMP integrates ten static source code metrics from object-oriented programming. MLR and PI implement feature selection, and the SMP's performance is evaluated based on accuracy and the Mean Magnitude of Relative Error (MMRE) parameters. Our findings are promising: for the User Interface Management System (UIMS) software, the proposed SMP demonstrates an impressive MMRE of 0.2441 and an accuracy of 91.91%. Similarly, for the Quality Evaluation System (QUES) software, an MMRE value of 0.2222 is achieved alongside a maximum accuracy of 80.95%. The ensemble method, when compared to other Machine Learning (ML) techniques, exhibits superior performance. These results affirm the effectiveness of our approach, contributing to the enhancement of software maintainability in object-oriented programming systems.

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Section: Introductionmentioning

confidence: 99%

Enhancing Software Maintainability Prediction Using Multiple Linear Regression and Predictor Importance

Yadav,

Singh

2023

IJERR

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Quality Metrics for Software Development Management and Decision Making: An Analysis of Attitudes and Decisions

Salin

Rybarczyk

Han

et al. 2022

Product-Focused Software Process Improvement

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Understanding the academic use of KM-driven Metaverse technology: insights from medical colleges

Abdulmuhsin,

Owain,

Alkhwaldi

2024

JSTPM

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Purpose This study delves into the behavioural intentions of educators within medical colleges at Mosul Universities concerning the adoption of Knowledge Management-Driven Metaverse technology (KM-D-MT). Rooted in an adapted Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, the research aims to enrich the understanding of Metaverse adoption factors, exploring correlations among key constructs such as performance expectancy, effort expectancy, social influence, facilitating conditions, perceived value, hedonic motivation and interaction. Furthermore, the study investigates the mediating roles of knowledge generation and knowledge sharing in the relationship between interaction and behavioural intention. Design/methodology/approach The research employs a quantitative approach, gathering 278 responses from educators in medical colleges. Structural Equation Modelling-Partial Least Squares (SEM-PLS) is used to analyse the data, rigorously examining the reliability and validity of research instruments. The investigation involves an extensive evaluation of various factors influencing educators’ intentions to adopt KM-D-MT, using a cross-sectional design. Findings The study reveals significant positive impacts of performance expectancy, effort expectancy, social influence, facilitating conditions, perceived value and hedonic motivation on behavioural intention to adopt KM-D-MT. Interaction is identified as a key factor positively influencing knowledge sharing and knowledge generation. Furthermore, knowledge sharing and knowledge generation exhibit positive correlations with behavioural intention. Interaction indirectly impacts behavioural intention through the mediating roles of knowledge generation and knowledge sharing, highlighting the transformative potential of Metaverse technology in reshaping knowledge processes. Practical implications The findings of this study hold practical implications for educators, institutions and policymakers. The adoption of KM-D-MT can enhance educational experiences, facilitate global collaboration and contribute to the continuous professional development of educators in medical colleges. Institutions are encouraged to strengthen technological and organisational infrastructure to support effective Metaverse implementation. Furthermore, promoting positive social norms, providing technical support and offering training programs can contribute to overcoming barriers and fostering a conducive environment for Metaverse adoption in medical education. Originality/value This research significantly contributes to theoretical perspectives by advancing Metaverse research and addressing the call for extensive studies covering theoretical, conceptual and empirical elements. It extends current UTAUT2 frameworks, exploring correlations in the context of medical education and contributes to knowledge management paradigms. The study’s originality lies in its exploration of Metaverse acceptance in higher education institutions, specifically in medical colleges in Iraq, providing valuable insights for further research and practical applications globally.

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How Software Quality Mediates the Impact of Intellectual Capital on Commercial Open-Source Software Company Success

Cited by 5 publications

References 80 publications

Enhancing Software Maintainability Prediction Using Multiple Linear Regression and Predictor Importance

Enhancing Software Maintainability Prediction Using Multiple Linear Regression and Predictor Importance

Quality Metrics for Software Development Management and Decision Making: An Analysis of Attitudes and Decisions

Understanding the academic use of KM-driven Metaverse technology: insights from medical colleges

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