Code modernization has become increasingly important as we move toward integrated cloud-based and virtualized software environments. Modernization of code permits legacy applications to continue functioning efficiently and securely without a complete rewrite. This makes it possible to meet the demands of today's infrastructure requirements without high cost or compromises in security or functionality. Companies need to expand services, access improved processes and use resources more efficiently. This demands architectural changes. Bringing these trends together, containerization under Docker has created a new model for application deployment that provides numerous advantages to program operation and interoperability, but requires special accommodation.
 

Modernization of code demands a high degree of precision. It is absolutely critical that the reengineered software performs in the same manner as the original. This requires two things: a rigorous approach to code refactoring based upon tried principles which retain the underlying logic; and a well-planned and consistent program of testing to ensure that logic is preserved and improvements do not in any way alter the function of the code. Testing is vital. Companies need to be certain that their modernized critical software will perform according to exact the same rules as the original.

Cybersecurity in the government has certainly come into focus recently as we have witnessed sensitive document troves exposed on WikiLeaks along access to government information from transaction systems. Perhaps the most serious of the latter was the 2015 incident in which the Federal Human Resources database was hacked for more than 22 million records containing sensitive personal information such as Social Security numbers. In this incident the source of the problem was 30-year-old mainframe software written in COBOL that was too technically obsolete to encrypt personal information. This raised the discussion of legacy system security to a new level.

Code modernization can create some interesting problems these days as business and government attempt to upgrade legacy systems. The route ahead is not always clear. Legacy systems may be in a variety of different languages installed for different purposes to meet the needs of the time. Even the transformation target itself can be problematic. Whether code is better translated into Java, a C variant, Python or something else demands consideration of the environment in which the code will reside, its interconnections, as well as the characteristics of the output language and its suitability. Economics rises as an important issue with older or obscure languages that do not have a breadth of available talent.