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SBSE

SBSE. Course 3. EA applications to SE. Analysis Design Implementation Testing Reference: Evolutionary Computing in Search-Based Software Engineering Leo Rela http://www2.lut.fi/~rela/dtyo_Leo_Rela.pdf. Overview: Analysis. Record and elicit customer requirements

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SBSE

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  1. SBSE Course 3

  2. EA applications to SE • Analysis • Design • Implementation • Testing Reference: Evolutionary Computing in Search-Based Software Engineering Leo Rela http://www2.lut.fi/~rela/dtyo_Leo_Rela.pdf

  3. Overview: Analysis • Record and elicit customer requirements • Understand customer requirements • No technical decisions about the system’s implementation • Includes feasibility study

  4. Overview: Design • Translate requirements into a representation of software • Focuses on • Data structures • Architecture • Interfaces • Algorithmic details • Also include • Resource and task allocation in a distributed system

  5. Overview: Implementation • The software design is translated into a computer program • Two facets • Produce computer programs • Support work of computer programmers • Typically, GP falls into this class

  6. Overview: Testing • Validation and Verification • Tackles problems like: • Test case generation • Find inputs that cause failures • Find inputs that cause long running time

  7. Applications: Analysis • Prediction of software failures • Exploring difficulty of the problem • Software project effort prediction • Project management

  8. Applications: Design • Multiprocessor scheduling • Task and resource allocation in distributed systems • Hardware/software co-design in embeded systems • Protocol construction • Architecture design

  9. Applications: Implementation • Automatic programming • N-version programming • Search for compiler optimization • Re-engineering

  10. Applications: Testing • Structural (White-box) testing • Functional (Black-box) testing • Integration test design • Testing based on mutation analysis • Search for response time extremes

  11. Analysis

  12. Prediction of Software Failures • Fixing failures is expensive • In testing → extra coding effort • After deployment → even more expensive • Applying reliability techniques is expensive • Quality prediction methods identify which parts of the system need reliability techniques

  13. ◙ GP-based software quality prediction • Modules are clasified as error-prone and not error prone • GP is used to predict the number of expected faults, but only the resulted ranking is used as a base for decision • For each module, a series of metrics are recorded • For some modules, the reliability has been measured • Can we predict, using the metrics, which will be the reliability for an yet untested program? • Metrics used: n. of operators/operands, LOC, LOEC, cyclomatic complexity

  14. ◙ Genetic programming model for software quality classification • Metrics • number of times the source code was inspected • number of LOC for different production phases • final number of commented code. • Implementation note: not LISP, but pointers to functions in C

  15. ◙ Automated Knowledge Acquisition and Application for SoftwareDevelopment Projects • Fuzzy system which classifies modules by probability of containing errors

  16. ◙ Combining Software Quality Predictive Models: An Evolutionary Approach • Various studies have used various methods/metrics • Combine the resulting experts into one which is able to work with partial input data. • AdaBoost: Gives weights to how hard is a specific data instances, and to how good is an expert. • GP: Combines decision trees

  17. Neural Networks / GA • ◙Using the genetic algorithm to build optimal neural networks for fault-prone module detection • GA generates NN (structure, weights) • ◙Evolutionary neural networks: a robust approach to software reliability problems

  18. Exploring difficulty of the problem • In early phases developers are not aware of potential (hard) problems ahead • Uncertainty about the real nature of the task • Bad decisions, made early, are difficult to fix • Possible solution: collect more knowledge about the problem to be solved • software problem exploration using genetic programming (SPE-GP)

  19. ◙Genetic Programming as an Explorative Tool in Early Software Development Phases • GP is used to try to solve the problem • For each input data, it is recorded how often the resulted programs fail to work correctly

  20. Software project effort prediction • Software is the most complex part of the system • It is also the most expensive to obtain • Goal: estimate cost and effort for a given project • Related: COCOMO (2)

  21. ◙ Limits of the Methods in Software Cost Estimation • Regression, GP, NN for software cost estimation • Estimations are not too good! • Poor methods / need new methods? • Incomplete/Inconsistent input data?

  22. ◙Can genetic programming improve software effort estimation? A comparative evaluation • Data from 81 Canadian software companies • Developer/manager experience, year of completion, attributes regarding size and complexity, effort (person-hours). • ANN and GP performed better.

  23. ◙ Software Project Effort Estimation Using Genetic Programming • Grammar Guided Genetic Programming • Classic GP: “closure”: any non-terminal should be able to handle as an argument any data type and value returned from a terminal or non-terminal. • Other alternative: Strongly Typed GP

  24. Other articles • ◙An evolutionary approach to estimating software development projects • Combination of Software Project Simulator and GAs. SPS generates input data which is then used by the GA for learning and prediction. • ◙ A validation of the component-based method for software size estimation.

  25. Project management • Manager has resources (time, budget, team etc.) and goal. • Task: meet the goal with the given resources • Or: which resources are needed to meet the goal? • Or: what can be accomplished with the given resources

  26. Tools • Gantt charts

  27. Tools (2) • TPG (Task Dependency Graph) • MM: Man Month • SR: Skill Required

  28. Software Project Management Net • Automatic allocating / scheduling based on GA • Input • TPG • Employee/skill database • Output • Schedule

  29. ◙ Genetic Algorithms for Project Management • Many-to-many relation between task and employees • Partial commitment • Objectives • Validity of job assignment • Minimum overtime • Minimum cost • Minimum time span

  30. Tasks • Read the survey • Skim over the articles • Like one? Choose it! • You are not supposed to like a 2-page article, unless you can implement the techniques described in it. • Don’t like any? Find your own SBSE article on the net and talk to me about it.

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