Exploring Strategies for Early Identification of Risks in Information Technology Projects

Project managers must ensure risk management and business justification for their projects. Approximately, 53% of IT projects failed due to project managers not identifying risks early in the projects\u27 lifecycle. The purpose of this single case study was to explore strategies IT project managers utilize to identify risks early in the project\u27s lifecycle. The study population consisted of 5 lead IT project managers from a telecom company located in the Midwest region of the United States who had managed IT projects. The conceptual framework that grounded this study was the general systems theory. The data collection process involved semistructured interviews, a review of public documents, and member checking interviews to verify the authenticity of the participants\u27 information. The data analysis process included the methodological triangulation, through interviewing and reviewing documents as well as using Yin\u27s 5-step process for analyzing data to identify codes and themes. After the data analysis, the themes that emerged were self-development tools and risk identification (inputs, project tools and techniques, and output). The findings indicated it is crucial that the project team and all stakeholders who have an interest in the project continuously address risk management throughout the project\u27s lifecycle. The implications for positive social change may help individuals understand risks better, interpret situations, and prevention of risk, which are essential to encourage economic inclusion, social protection, and environmental building

A survey on software testability

Context: Software testability is the degree to which a software system or a unit under test supports its own testing. To predict and improve software testability, a large number of techniques and metrics have been proposed by both practitioners and researchers in the last several decades. Reviewing and getting an overview of the entire state-of-the-art and state-of-the-practice in this area is often challenging for a practitioner or a new researcher. Objective: Our objective is to summarize the body of knowledge in this area and to benefit the readers (both practitioners and researchers) in preparing, measuring and improving software testability. Method: To address the above need, the authors conducted a survey in the form of a systematic literature mapping (classification) to find out what we as a community know about this topic. After compiling an initial pool of 303 papers, and applying a set of inclusion/exclusion criteria, our final pool included 208 papers. Results: The area of software testability has been comprehensively studied by researchers and practitioners. Approaches for measurement of testability and improvement of testability are the most-frequently addressed in the papers. The two most often mentioned factors affecting testability are observability and controllability. Common ways to improve testability are testability transformation, improving observability, adding assertions, and improving controllability. Conclusion: This paper serves for both researchers and practitioners as an "index" to the vast body of knowledge in the area of testability. The results could help practitioners measure and improve software testability in their projects