Software project failures prediction using logistic regression modeling

Ibraigheeth, Mohammad Ahmad; Fadzli, Syed Abdullah

doi:10.1109/iccis49240.2020.9257648

Cited by 7 publications

(6 citation statements)

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“…Failure prediction methods identify the factors and risks that affect a project's success or failure by reviewing and investigating resources and analyzing the relationship between predictors and outcomes [12]. A causal model for software project failure should completely determine the causal relationships affecting failure [11].…”

Section: B Failure Prediction Methodsmentioning

confidence: 99%

“…It produces root causes related to all process areas such as implementation, requirements, management, software testing, and deployment [11], [21]. Additionally, logistic regression is another method to help project managers to assess expected failures [12]. In risk analysis and RCA, the root causes at the deepest level may or may not be related to team behaviors, although some studies categorize failure factors and examine management as a separate category [11].…”

Section: B Failure Prediction Methodsmentioning

confidence: 99%

“…The definitions of root causes are also similar such as unrealistic project objectives, team technical problems, lack of user involvement, requirement instability, problematic technology, etc. [12].…”

Section: B Failure Prediction Methodsmentioning

confidence: 99%

“…However, the causes of failure are often investigated in isolation [11]. The influence of failure factors on the result of a project and the relationship between them were evaluated by conducting root cause analysis (RCA) [11], logistic regression [12], grounded theory, and latent Dirichlet allocation [13]. Researchers have generally focused on defining the causal relationships between failure factors, not explaining the team behaviors behind the failure factors.…”

Section: Introductionmentioning

confidence: 99%

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Software Project Failure Process Definition

Nizam

2022

IEEE Access

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Many researchers have attempted to identify the factors behind software project failures and their solutions from various perspectives. However, systematic and integrated process definitions of failure as process models for success are lacking. This study aims to build a process definition for software project failure as an anti-pattern by identifying the main phases and their relationships in terms of team behavior. We researched software engineering literature and case studies to gather information about critical incidents and repeating behaviors of teams in failed projects into a novel dataset. Grounded theory was employed to build a theoretical foundation for failure phase definitions from the collected data. The design structure matrix and Bayesian belief network were used for the quantitative assessment of the transitions between phases. The results revealed that common behavioral patterns occurred in approximately 89 percent of the case studies, supporting the decision to consider software project failure as a process. The proposed failure process definition has a simple structure that uses everyday concepts for phase names and reveals the critical behaviors leading a software project to failure Thus, it provides critical insights for software professionals, non-technical stakeholders, and managers to evaluate the progress of their projects and design strategies to avoid failure.INDEX TERMS Bayesian belief network, grounded theory, qualitative process analysis, software process models.

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Section: B Failure Prediction Methodsmentioning

confidence: 99%

Section: B Failure Prediction Methodsmentioning

confidence: 99%

Section: B Failure Prediction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Software Project Failure Process Definition

Nizam

2022

IEEE Access

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“…Assessing the probable software project failure early during development process can mitigate the effect of the undesirable events that could lead to project failure [10]. The paper aims to develop new weighted ensemble predictive model which use historical failure data gathered from several past software projects to accurately predicting possible failures in future software projects.…”

Section: Introductionmentioning

confidence: 99%

A New Weighted Ensemble Model to Improve the Performance of Software Project Failure Prediction

Ibraigheeth,

Eid,

Alsariera

et al. 2024

IJACSA

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The development of a software project is frequently influenced by various risk factors that can lead to project failure. Predicting potential software project failures early can aid organizations in making decisions regarding possible solutions and improvements. This paper proposes a software project failure prediction model based on a weighted ensemble learning approach. The proposed model aims to determine the failure probability as well as the expected project outcome (Success/Failure). Various ensemble approaches, such as simple majority voting, can be employed in predicting software project failure. However, in majority voting algorithms, all base models have the same weights, resulting in an equal effect on the final prediction result, regardless of their predictive abilities. Our proposed algorithm assigns higher weights to base models that demonstrate a greater ability to correctly predict more challenging data instances. The proposed model is developed based on a dataset gathered from real previous software project reports, comprising both successful and failed projects, to provide evidence supporting the predictive model's capabilities and to obtain high-confidence results. The performance of the developed model is comprehensively assessed through various measures, revealing its superiority in predicting software project failures compared to both simple majority voting and individual models. This research suggests that the proposed model can be integrated into the software system development process, spanning requirement analysis, planning, design, and implementation phases, to evaluate the project's status and identify potential risks.

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Software Defect-Based Prediction Using Logistic Regression: Review and Challenges

Goyal

Sinha

2021

Second International Conference on Sustainable Technologies for Computational Intelligence

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Software project failures prediction using logistic regression modeling

Cited by 7 publications

References 6 publications

Software Project Failure Process Definition

Software Project Failure Process Definition

A New Weighted Ensemble Model to Improve the Performance of Software Project Failure Prediction

Software Defect-Based Prediction Using Logistic Regression: Review and Challenges

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