2.1.2 Thinking Ahead

• Inputs and Outputs


  • Designing a solution requires thinking ahead about how the different components of a problem can be handled in the best possible way
  • Developers can build programs that are easy and intuitive to use
• Inputs


  Data required to solve the problem, entered into the system by the user
• Outputs


  Results that are passed back, the solution to the problem
• All computational problems consist of inputs which are processed to produce an output
• Evaluating inputs and outputs

  • Consider data structures and data types involved
  • Consider input and output devices
• Designing a solution


  1. Consider the outputs based on the user's requirements

  2. Identify the inputs required and how these need to be processed to achieve these outputs
• Preconditions


  Requirements which must be met before a program can be executed
• Preconditions


  • Can be tested for within the code
  • Included in the documentation accompanying a particular subroutine, library or program
• Specifying preconditions


  • Expects the arguments passed to it to meet certain criteria
  • Reduces the length and complexity of the program
  • Saves time spent on debugging and maintenance
  • Makes subroutines more reusable
• Reusable program components


  Commonly used functions packaged into libraries for reuse
• Reusable components


  • Abstract data structures (e.g. queues and stacks)
  • Classes
  • Subroutines (e.g. functions and procedures)
• Reusing program components


  • Problem decomposition is used to identify where previously-developed program components can be reused
  • More reliable than newly-coded components, as they have already been tested
  • Save time, money and resources
  • May need to be modified to be compatible with existing software
• Caching


  Storing instructions or values in cache memory after they have been used, as they may be used again
• Benefits of caching


  • Saves time of retrieving instructions from secondary storage again
  • Frees up bandwidth for other tasks on a network
• Prefetching


  Algorithms predict which instructions are likely to soon be fetched and are loaded and stored in cache
• Drawbacks of caching


  • Limited by accuracy of algorithms used, as data stored in cache is not always used
  • Effectiveness depends on caching algorithm's ability to manage the cache
  • Larger caches take a long time to search, but smaller cache sizes limit how much data can be stored
  • Can be difficult to implement well