of all individual tasks in real time including the files each coder selects to either edit or to view for a particular task the file view edit information is then leveraged to collect the following task pair properties note properties may vary for different software systems within same file within same platform within same operating system number of slsms number of als within same software requirement within same software requirement functional area within same software requirement technical area these properties are all known at the time work begins on each task and can be captured real time as work progresses therefore by monitoring these metrics or others that may better define a specific software system critical potential coordination requirements can be identified in a timely manner these potential coordination requirements can then be evaluated against a baseline set of u201c known parameters u201d to determine if the potential coordination requirement is sufficiently critical to alert the coders of the involved tasks requiring coordination thus the coders are able to resolve the coordination requirement and prevent future rework or errors in the final software system svm machine learning the properties decided upon for each software system are then used to create a baseline u201c region u201d of critical coordination requirements in a machine learning environment this region is a multi dimensioned space that correlates to the task pair properties that define a task pair as requiring or not requiring coordination the system uses a support vector machine svm classification technique an svm is a supervised machine learning classification algorithm given a training set it produces a model that can be used to predict the classification of unknown instances given a set of known parameters of those unknown instances the known parameters are historical task pair properties with known coordination requirements discussed.