The spreadsheet dominates the pharmacoeconomics field as the software platform of choice for programming pharmacoeconomic models that are submitted to reimbursement agencies worldwide. Arguably, the spreadsheet represents a poor choice for model development and implementation as standard software development tools to manage spreadsheet quality are non-existent. This paper will demonstrate how the concept and principles of software engineering and adopting their best practices in model development can increase model transparency and our confidence that our models perform as expected.  We will also compare the advantages and disadvantages of using spreadsheets versus other programming languages.  Briefly, the primary advantage of the spreadsheet is that it’s universally available and accessible to all.  It’s universal in that a spreadsheet is available on most computers.  It’s accessible in that everyone can open the model, inspect it, and use it.  Beyond these advantages, the structure of the spreadsheet challenges our model code to be intention revealing and transparent.  For example, generating an audit trail showing the history of what changes were made, when, and by who is not possible with spreadsheet files. Moreover, the spreadsheet does not have available standard software tools for developing test suites.  A test suite represents a series of tests that must be passed to confirm software performs as expected.  For models, this would mean the test suite confirms a model computes what it purports to compute.  Hence, test suites serve a critical and foundational function in the lifecycle of modern software development and can serve a similar role in pharmacoeconomic model development.  In summary, adopting traditional programming languages and their software engineering support tools offers an opportunity to increase model transparency compared with spreadsheets.  Reimbursement agencies should consider broadening their software guidances to include non-spreadsheet programming languages to increase model transparency and our confidence that pharmacoeconomic models perform as expected.