Computer-implemented methods and systems for measuring, estimating, and managing economic outcomes and technical debt in software systems and projects

A computer-implemented method of determining a technical debt of a computer software codebase, comprising the steps performed by one or more computer systems of:

• (a) scanning the codebase using an analysis tool to capture and store information about the structure, properties, or metrics of the codebase;
• (b) inputting the information captured in (a) into one or more fitted statistical models to generate software economic output metrics for the software codebase, said software economic output metrics including defect density projections and/or developer productivity projections for the codebase over a period of time the codebase is expected to be in service, wherein said one or more fitted statistical models are based on custom fitted models providing estimates for low level software economic parameters, said custom fitted models created by applying statistical regression techniques to code qualify metrics, design quality metrics, and/or test quality metrics, wherein said custom fitted models are refined using correlation significance, verifying outcomes of refined models against available real data, and adding information on the verified outcomes to the codebase as a new point of information to refine the one or more fitted statistical models or the custom fitted models;
• (c) calculating a total cost of development including costs associated with fixing defects and developing new features based on the defect density projections and/or developer productivity projections over the period of time;
• (d) calculating a capitalized value of the costs calculated in (c) based on a given interest rate to determine the technical debt of the codebase;
• (e) outputting the technical debt;
• (f) exploring the impact, using a software tool or graphical user interface, of a code quality improvement initiative, a design quality improvement initiative, or a test quality improvement initiative by adjusting code quality inputs, design quality inputs, or test quality inputs to the one or more fitted statistical models to generate updated software economic output metrics including updated defect density projections and/or updated developer productivity projections for the codebase, and repeating (c), (d), and (e) based on the updated defect density projections and/or updated developer productivity projections;
• and
• (g) performing a code quality improvement, a design quality improvement, or a test quality improvement of the codebase by redesigning the codebase, rewriting the codebase, refactoring the codebase, or improving automated testing of the codebase based on the technical debt in (e).

A computer-implemented method of analyzing a computer software codebase, comprising the steps performed by one or more computer systems of:

• (a) scanning the codebase using an analysis tool to capture and store information about the structure, properties, or metrics of the codebase;
• (b) inputting the information captured in (a) into one or more fitted statistical models to generate first level software economic output metrics for the software codebase, said first level software economic output metrics including at least one of:
• defect density or developer productivity projections for the codebase, wherein said one or more fitted statistical models are based on custom fitted models providing estimates for low level software economic parameters, said custom fitted models created by applying statistical regression techniques to code qualify metrics, design quality metrics, and/or test quality metrics, wherein said custom fitted models are refined using correlation significance, verifying outcomes of refined models against available real data, and adding information on the verified outcomes to the codebase as a new point of information to refine the one or more fitted statistical models or the custom fitted models;
• (c) receiving additional information including at least one of:
• benchmark data collected from analysis of other codebases unrelated to said codebase, information related to version control or change management systems, and user input parameters;
• (d) using the first level software economic output metrics generated in (b) and the additional information received in (c) to generate second level software economic output metrics including at least one of:
• metrics related to maintainability, agility, cost, risk, defects, waste, security, technical debt, and schedule, and outputting the second level software economic output metrics;
• and
• (e) performing a code quality improvement, a design quality improvement, or a test quality improvement of the codebase by redesigning the codebase, rewriting the codebase, refactoring the codebase, or improving automated testing of the codebase based on the generated second level software economic output metrics in (d).