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A Hierarchical Measure for Software Complexity

A Hierarchical Measure for Software Complexity. Dirk Beyer Jeff Jiang Presented by: Ashgan Fararooy. Abstract. The complexity of the software product can affect software quality A new simple metric is presented to determine the complexity of a software system

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A Hierarchical Measure for Software Complexity

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  1. A Hierarchical Measure for Software Complexity Dirk Beyer Jeff Jiang Presented by: Ashgan Fararooy

  2. Abstract • The complexity of the software product can affect software quality • A new simple metric is presented to determine the complexity of a software system • Based on variables defined and used in source code and arguments involved in each function call

  3. Overview Software Complexity Metrics* • McCabe’s measure (Cyclomatic complexity) • measures the number of linearly independent paths through a program's source code • Halstead’s measures (computed statically) • Program length: N = N1 + N2 • Program vocabulary: n = n1 + n2 • Volume: • Difficulty : D = (n1/2)  (N2/n2) • Effort: E = D  V *From Wikipedia

  4. Proposed Metric • Definition Evaluating software complexity based on the following factors: • The number of variables defined and used in a software system • The number of arguments (parameters) involved in each function call in the source code

  5. Motivation A source code which utilizes more variables: • Intuition:Is harder to understand, maintain, and migrate • Justification: Considering most modern programming languages are strong typed, it requires more attention regarding: • Initialization • Type checking • Type conversion • Value tracking • Deconstruction

  6. Motivation • The same argument applies for function parameters • Function arguments (parameters) can be considered variables • Function is supposed to validate all parameters passed into it and handle all exceptions • A function with a large number of parameters is hard to understand, debug, and maintain

  7. Implementation • Developed an Eclipse plug-in tool for measuring complexity • Goals: • Evaluating the proposed metric • Computing and comparing the complexity of different Java source codes based on the metric

  8. Architecture and High Level Design

  9. Complexity Evaluation Rules • I.In a statement that is not a variable declaration, each variable used will contribute one point to complexity index • II.In a statement that is not a function declaration, each method/function invocation will contribute n points to complexity index (n = the number of arguments) • III.Class member attributes will be treated as variables except in their declaration statements

  10. Validity • Does the proposed metric really reflect the complexity degree of programs? • Through a series of experiments and tests, the proposed measure was evaluated • This was done by evaluating the complexity index and studying the difference between different revisions • The revisions of the sample programs provide the same functionality, but the code is simplified

  11. Case Study • Testing the software tool and evaluating the proposed measure • Merge sort and Quick sort are more complex than Bubble sort and Insertion sort • Although the first two have better running times, but their source code is indeed more complicated

  12. Case Study

  13. Further Work • Revising/Refining the point assignment system in complexity evaluations rules • Taking nested loops into account for complexity measurement • Considering operators in addition to function calls? • Improving the plug-in and adding more features • Support for other languages

  14. Thank you

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