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The Future of Software Engineering: Tools

Explore the importance of tools in software engineering, including reproducibility, published systems, and the impact of commercialization. Learn about static and dynamic analysis, lightweight tools, and the role of case studies in publications.

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The Future of Software Engineering: Tools

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  1. The Future of Software Engineering: Tools Michael Ernst MIT Lab for Computer Science http://sdg.lcs.mit.edu/~mernst/

  2. Tools matter The most productive people are the most effective tool users Tools are a change agent

  3. Reproducibility Replication is key to the scientific method Papers may gloss over details Insist on published systems Permits additional evaluation Stop accepting papers without supporting data and implementations Central repository for such information Need support for infrastructure-building

  4. Counterargument to publication Responsibility to have commercial impact Can have commercial impact without IP If it's a good idea, it will be adopted Secrecy is antithetical to science & education

  5. Evaluation of implementation Tools should work • for users other than implementers • when run on production systems Correct the impression that software engineers do not build real systems

  6. Evaluation of tools Humans complicate systems Theorems much easier to understand! Case studies, not experiments Devise rewards for reproducing experiments Special publication venues? Do not raise the bar for new ideas Industry can assist academia

  7. Ignore the average programmer Practitioner/researcher gap What is the job of researchers? Researchers must assume best practices, in order to have long-term impact

  8. Static and dynamic analysis How to obtain information: • Programmer-supplied • Static analysis: examine the program text • properties are guaranteed to be true • pointers are intractable in practice • Dynamic analysis: run the program • efficient, precise • complementary to static techniques

  9. Combining techniques Use static to help dynamic and vice versa Transfer from one domain to the other Example: Purify and LCLint Combine in a principled way Select the desired efficiency/soundness

  10. Lightweight tools No need to produce exact results Useful results that people can check

  11. The return of formal specifications Full formal specifications do not work Partial formal specifications: • Specify only certain aspects of behavior • Generated automatically Other examples from languages and compilers Example: functional programming

  12. Summary Tools are key Publication must include tools Case studies Assume best practices Static and dynamic analysis Lightweight tools

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