The George Washington University School of Engineering and Applied Sciences Engineering Management and System Engineering Dept. Combating Software Project Failure: A Predictive Analytics Framework to Improve Software Testing and Product Quality Authors: Gina Guillaume-Joseph, PhD Candidate Dr. James Wasek Dr. Enrique Campos-Nanez, and Dr. Pavel Fomin

Introduction Predictive Analytics is a data driven technology used to predict and influence the future. We develop a Predictive Model that determines failure points in the SELC and relates them to specific causal factors of testing. Our work attempts to optimize project data and information to provide informed and real-time decisions that combat financial risks incurred with failed projects.

Review Ewusi-Mensah, 2003 offers an empirically grounded study on software failures and proposes a framework of abandonment factors1 that highlight risks and uncertainties present in the SELC phases of a software project. Takagi et al, 2005 analyzed the degree of confusion2 of several software projects using logistic regression analysis to construct a model to characterize confused2 projects. 1 Ewusi-Mensah, Kweku. 2003. “Software Development Failures.” MIT Press (1): 187-187. 2 Takagi, Yasunari, Osamu Mizuno, and Tohru Kikuno. “An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis.” Empirical Software Engineering, volume 10, number 4, pages 495- 515, December 2005.

Methodology This work introduces the Project Testing Confidence Metric (PtcM) and the corresponding Predictive Model. The Model developed from data of software project failures and successes is based on a framework that identifies significant influencing failure factors and impact on the four major phases of the SELC.

Methodology The failure factors in the testing phase have the greatest impact on software project failure. The variables are used to develop the Model.

Importance Software Project failures are costly and often result in an organization losing millions of dollars due to termination of a poor quality project (Jones, 2012). Software engineering is a risky endeavor whose outcome often cannot be predetermined. Software Testing is a critical component of mature software engineering; however, project complexities make it the most challenging and costly phase of the Systems Engineering Lifecycle (SELC) (Jones, 2012). Jones, Capers. “Software Quality Metrics: Three Harmful Metrics and Two Helpful Metrics”; June 2012; Retrieved from website: http://www.ppi-int.com/systems- engineering/free%20resources/Software%20Quality%20Metrics%20Capers%20Jones%20120607.pdf.

Preliminary Results The Predictive Model leverages a development organization’s past project performance to predict outcomes of future work. The PtcM uses that data to determine the effectiveness of testing by correlating previous project failure with inadequate testing to isolate those areas for improvement.

Preliminary Results The Predictive Model and the resulting PtcM provide the organization’s leadership insight into determining which projects to embark upon within the project portfolio.

Conclusion The Predictive Model and the PtcM will assist in maturing an organization’s testing and quality assurance capabilities by implementing institutional learning. By predicting the likelihood of project failure during the early planning phase, this work will promote a more successful project portfolio for the organization. Our work helps organizations answer the question, “What will happen in the future and how can we act on this insight?”

Ms. Gina Guillaume-Joseph Thank You Ms. Gina Guillaume-Joseph The MITRE Corporation Systems Engineering, Ph.D. Candidate The George Washington University Contact: ginagj@gwu.edu