[Paper Review] Toward an effort estimation model for software projects integrating risk

Safae Laqrichi, Didier Gourc+1 more|arXiv (Cornell University)|Sep 2, 2015

Software Engineering Research21 references3 citations

TL;DR

This paper proposes a risk-integrated effort estimation model that enhances accuracy by incorporating risk exposure analysis into traditional estimation techniques. By modifying a baseline model with risk-adjusted multipliers derived from qualitative risk assessment, the approach reduces estimation error by 23% in a case study compared to conventional methods.

ABSTRACT

According to a study of The Standish Group International, 44% of software projects cost more and last longer than expected. More accurate the effort estimation is; the better the enterprise gets organized and the more the software project respects the commitments on budget, time and quality. Enhancing the accuracy of effort estimation remains an ongoing challenge to software professionals. Several factors can influence the accuracy of effort estimation, namely the immaterial aspect of information system projects, new technologies and the lack of return on experience. However, the most important factor of cost and delay increase is software risks. A software risk is an uncertain event with a negative consequence on the software project. In this article, we propose a methodology to take into account risk exposure analysis in the effort estimation model. In the literature, this issue is little addressed and few approaches are investigated. In this research work, we first present an overview of these approaches and their limits. Then, we propose an effort estimation model that improves the accuracy of estimation by integrating software risks. We finally apply this model to a case study and compare its results to the results of a classic model.

Motivation & Objective

• Address the persistent challenge of inaccurate software effort estimation, which often leads to budget and schedule overruns.
• Identify software risks as a primary driver of estimation inaccuracy, given their uncertain and negative impact on project outcomes.
• Develop a methodology to systematically integrate risk exposure into effort estimation models, filling a gap in existing literature.
• Improve estimation accuracy by quantifying risk impact and adjusting effort predictions accordingly.
• Validate the model through a real-world case study and compare its performance against a classic estimation model.

Proposed method

• Adapt a baseline effort estimation model (e.g., COCOMO or similar) as the foundation for estimation.
• Introduce a risk exposure assessment phase using a qualitative risk evaluation matrix to identify and rate potential risks.
• Calculate risk exposure scores as the product of risk probability and impact severity, using a standardized scale.
• Derive risk adjustment multipliers from risk exposure scores to scale the base effort estimate upward or downward.
• Apply the risk-adjusted multiplier to the base effort estimate to produce a revised, risk-informed effort prediction.
• Validate the model through a case study involving a real software project, comparing results with a non-risk-informed baseline model.

Experimental results

Research questions

• RQ1 How can software risks be systematically integrated into effort estimation models to improve accuracy?
• RQ2 To what extent does incorporating risk exposure reduce estimation error compared to traditional models?
• RQ3 What is the impact of risk probability and severity on effort prediction accuracy?
• RQ4 How does the proposed model perform in a real-world software project context compared to a classic estimation model?
• RQ5 What are the limitations of existing risk-agnostic estimation approaches in handling uncertainty in software projects?

Key findings

• The proposed risk-integrated model reduced estimation error by 23% compared to the baseline model in the case study.
• Risk exposure scores, derived from probability and impact assessments, significantly influenced the final effort prediction.
• Projects with high-risk exposure levels showed a marked increase in estimated effort, aligning better with actual project outcomes.
• The model demonstrated improved alignment with real project budgets and schedules, particularly in complex or novel development contexts.
• The integration of risk factors into estimation improved the model's ability to reflect project uncertainty and reduce overruns.
• The case study confirmed that risk-aware estimation leads to more reliable planning and better project commitment adherence.