Search based software engineering 10 years

Apparently, Search Based Software Engineering or SBSE celebrates 10 years as a research area this year. A bibliometric analysis of 740 contributions was recently published in honor of this landmark. I wonder if my own small contribution is included there?

References

• [2011,incollection] bibtex
  F. de Freitas and J. de Souza, "Ten Years of Search Based Software Engineering: A Bibliometric Analysis," , Cohen, M. and Cinnéide, Eds., Berlin, Heidelberg: Springer Berlin / Heidelberg, 2011, vol. 6956, pp. 18-32.
  @incollection{citeulike:9729612, abstract = {Despite preceding related publications, works dealing with the resolution of software engineering problems by search techniques has especially risen since 2001. By its first decade, the Search Based Software Engineering ({SBSE}) approach has been successfully employed in several software engineering contexts, using various optimization techniques. Aside the relevance of such applications, knowledge regarding the publication patterns on the field plays an important role to its understanding and identity. Such information may also shed light into {SBSE} trends and future. This paper presents the first bibliometric analysis to {SBSE} publications. The study covered 740 publications of the {SBSE} community from 2001 through 2010. The performed bibliometric analysis concerned mainly in four categories: Publication, Sources,
  authorship, and Collaboration. Additionally, estimates for the next years of several publication metrics are given. The study also analyzed the applicability of bibliometric laws in {SBSE},
  such as Bradfords and Lotka.},
  address = {Berlin, Heidelberg},
  author = {de Freitas, Fabr\'{\i}cio and de Souza, Jerffeson},
  chapter = {5},
  citeulike-article-id = {9729612},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-23716-4_5},
  citeulike-linkout-1 = {http://www.springerlink.com/content/q2tr783534pj4444},
  doi = {10.1007/978-3-642-23716-4_5},
  editor = {Cohen, Myra and Cinn\'{e}ide},
  isbn = {978-3-642-23715-7},
  keywords = {20110831a},
  pages = {18--32},
  posted-date = {2011-08-31 09:57:19},
  priority = {1},
  publisher = {Springer Berlin / Heidelberg},
  series = {Lecture Notes in Computer Science},
  title = {Ten Years of Search Based Software Engineering: A Bibliometric Analysis},
  url = {http://dx.doi.org/10.1007/978-3-642-23716-4_5},
  volume = {6956},
  year = {2011}
}
• [2010,article] bibtex
  W. Afzal, R. Torkar, R. Feldt, and G. Wikstrand, "Search-based Prediction of Fault-slip-through in Large Software Projects," , pp. 79-88, 2010.
  @article{citeulike:9729627, abstract = {A large percentage of the cost of rework can be avoided by finding more faults earlier in a software testing process. Therefore, determination of which software testing phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of five different techniques, namely particle swarm optimization based artificial neural networks ({PSO}-{ANN}), artificial immune recognition systems ({AIRS}), gene expression programming ({GEP}), genetic programming ({GP}) and multiple regression ({MR}), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement potential in different testing phases by striving towards finding the right faults in the right phase. We have conducted an empirical study of two large projects from a telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of fit and absolute relative error measures. They indicate that the four search-based techniques ({PSO}-{ANN},
  {AIRS},
  {GEP},
  {GP}) perform better than multiple regression for predicting the fault-slip-through for each of the four testing phases. At the unit and function testing phases, {AIRS} and {PSO}-{ANN} performed better while {GP} performed better at integration and system testing phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with {GP} showing more consistent performance across two of the four test phases.},
  author = {Afzal, W. and Torkar, R. and Feldt, R. and Wikstrand, G.},
  booktitle = {Search Based Software Engineering (SSBSE), 2010 Second International Symposium on},
  citeulike-article-id = {9729627},
  citeulike-linkout-0 = {http://dx.doi.org/10.1109/ssbse.2010.19},
  citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5635180},
  doi = {10.1109/ssbse.2010.19},
  isbn = {978-1-4244-8341-9},
  keywords = {20110831a},
  pages = {79--88},
  posted-date = {2011-08-31 10:07:56},
  priority = {0},
  publisher = {IEEE},
  title = {Search-based Prediction of Fault-slip-through in Large Software Projects},
  url = {http://dx.doi.org/10.1109/ssbse.2010.19},
  year = {2010}
}

Generating Whole Test Suites with SBSE

I find this rather exciting, generating / optimizing your whole test suite automatically.

SBSE meets FST

Together with Wasif Afzal, Richard Torkar and Robert Feldt I have submitted a paper for publication at the SSBSE 2010 conference. The title of the paper is “Search-based prediction of fault-slip-through in large software projects”.

Auto-fix your bugs with UT and SBSE

Your mother probably taught you to write unit test cases for all your code? She probably insisted that you should write a new test case for every reported bug? You probably wondered why, but still followed her advice like any dutiful child?

Now, the answer is here. With the help of Search Based Software Engineering (SBSE) and Genetic Programming (GP) some researchers have shown how you can use all your hard earned test cases to auto-fix your bugs.