{'fraction_non_alphanumeric': 0.013847675568743818, 'fraction_numerical': 0.0009891196834817012, 'mean_word_length': 5.791946308724833, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Improving software project outcomes through predictive analytics: Part 1

The complex and emergent behavior of software systems makes information and data key components of this unpredictable environment. The use of a data-driven approach to identify and to accurately predict the sources of software project delays, cost overruns, failures, or successes may prove a significant contribution to the fields of systems engineering, software development and project management. Software project failures are pervasive and despite the research, failures still persist. This paper deals with the systems mindset in addressing failure to introduce a software-specific predictive analytics model that accurately predicts software project outcomes of failure or success. The use of an evidence-based approach to identify software project failure factors will result in better understanding of these phenomena that will ultimately improve software project success rates and minimize risks in systems engineering efforts.