Towards a Machine Learning Model for Predicting Failure of Agile Software Projects

Mohamed, Abeer A.; Darwish, Nagy Ramadan; Hefny, Hesham A.

doi:10.5120/ijca2017914466

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…In addition, they are used after the product has been delivered to estimate the effort and expenditure associated with maintenance [5]. Maintainability is heavily influenced by software design metrics [6].…”

Section: Introductionmentioning

confidence: 99%

A proposed model for detecting defects in software projects

Nabil Mahmoud,

Abdelaziz,

Santos

et al. 2024

IJEECS

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<span lang="EN-GB">Defective modules that cause software execution failures are common in large software projects. Source code for a significant number of modules may be found in several software repositories. This software repository includes each module’s software metrics and the module’s faulty status. Software companies face a considerable problem detecting defects in sizeable and complex programming code. In addition, many international reports, such as the comprehensive human appraisal for originating (CHAOS) report, have mentioned that there are countless reasons for the failure of software projects, including the inability to detect errors and defects in the programming code of those projects at an early stage. This research employs a statistical analysis technique to reveal the characteristics that indicate the faulty status of software modules. It is recommended that statistical analysis models derived from the retrieved information be merged with existing project metrics and bug data to improve prediction. When all algorithms are merged with weighted votes, the results indicate enhanced prediction abilities. The proposed statistical analysis outperforms the state-of-the-art method (association rule, decision tree, Naive Bayes, and neural network) in terms of accuracy by 9.1%, 10.3%, 13.1%, and 13.1%, respectively.</span>

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Section: Introductionmentioning

confidence: 99%

A proposed model for detecting defects in software projects

Nabil Mahmoud,

Abdelaziz,

Santos

et al. 2024

IJEECS

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An Agile Methodology for Ontology Development

Abdelghany¹,

Darwish²,

Hefni³

2019

IJIES

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Ontology is defined as an explicit specification of a shared conceptualization. Currently, ontology is used in semantic web, information retrieval, artificial intelligence, information systems, knowledge management, etc. The development of ontology involves a structural and logical complexity that is comparable to the development of software artifacts. Therefore, ontology building requires a methodology to ensure its reliability. In this context, there are several methodologies proposed for building ontologies. However, most of the existing methodologies failed to provide sufficient details for the activities and techniques employed in them with a defined ontology lifecycle. To build ontologies that are reliable, long lived and continually adapted, the ontology engineering (OE) should be supported by the software engineering (OE). But, SE was not initially meant to support the development of software artifacts such as ontologies. There is a significant gap between them in terms of popularity and maturity level. The aim of this paper is to bridge this gap by proposing an Agile Methodology for Ontology Development (AMOD). AMOD adopts the agile principles and practices in the ontology development. The final framework of AMOD fits the various ontology activities into the phases of the Scrum agile methodology. It has three phases: pre-game, development and post-game. AMOD was applied to develop ontology for software project time management. Additionally, a compliance analysis of different ontology methodologies with respect to the IEEE Standard was made. Results showed that AMOD resulted in 56% satisfaction for IEEE standard processes. This resembles 22% enhancement in the satisfaction against the other methodologies.

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Multiple Linear Regression for Determining Critical Failure Factors of Agile Software Projects

Abdelaziz¹,

Darwish²,

Hefny³

2019

IJIES

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The management of agile software projects plays an important role in the success or failure of projects. There are many factors of success or failure that clearly affect agile software projects. There is a big problem in not having a formal study to identify critical failure factors that assess the success or failure of projects accurately. This paper focuses on failure factors of agile software projects. This paper seeks to configure a formal study by using multiple regression analysis for identifying critical failure factors to avoid projects failure, thus saving time and money for the stakeholders. The proposed model is implemented by using R programming. The proposed model also is implemented by two models. The first model based on critical failure factors (CFF) of agile software projects. The second model based the failure factors of the proposed in the initial list (FFIL). The model of CFF outperforms on the model of FFIL in order to accuracy by 10.7% and random error ratio by 4.8%. The results revealed that only 10 out of 19 hypotheses were supported, identifying seven critical failure factors for Agile software development projects: (a) Organizational culture too traditional, (b) Lack of agile logistical arrangements,(c) Lack of project management competence, (d) Lack of team work, (e) Bad customer relationship, (f) defined project scope, and (g) defined project planning.

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Towards a Machine Learning Model for Predicting Failure of Agile Software Projects

Cited by 3 publications

References 11 publications

A proposed model for detecting defects in software projects

A proposed model for detecting defects in software projects

An Agile Methodology for Ontology Development

Multiple Linear Regression for Determining Critical Failure Factors of Agile Software Projects

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