Abstract
Background: secondary studies (SSs), in the form of systematic literature reviews and systematic mappings, have become a widely used evidence-based methodology to to create a classification scheme and structure research fields, thus giving an overview of what has been done in a given research field.
Problem: often, the conduction of high-quality SSs is hampered by the difficulties that stem from creating a proper “search string”. Creating sound search strings entails an array of skills and domain knowledge. Search strings are ill-defined because of a number of reasons. Two common reasons are (i) insuffient domain knowledge and (ii) time and resource constraints. When ill-defined search strings are used to carry out SSs, a potentially high number of pertinent studies is likely to be left out of the analysis.
Method: to overcome this limitation we propose an approach that applies a search-based algorithm called Hill Climbing to automate this key step in the conduction of SSs: search string generation and calibration.
Results: we conducted an experiment to evaluate our approach in terms of sensibility and precision. The results would seem to suggest that the precision and the sensibility our approach are 25.2% and 96.2%, respectively.
Conclusion: The results were promising given that our approach was able to generate and calibrate suitable search strings to support researchers during the conduction of SSs.
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Notes
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Control studies are papers that must be retrieved when search in a given database.
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References
Basili, V., Caldiera, G., & Rombach, H. (1994). The goal question metric paradigm (1st ed.). Wiley.
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3), 268–308.
Dieste, O., & Padua, A. (2007). Developing search strategies for detecting relevant experiments for systematic reviews. In ESEM 2007, Madrid (pp. 215–224).
Gay, G. (2010). A baseline method for search-based software engineering. In PROMISE 2010, PROMISE ’10, Timisoara (pp. 2:1–2:11). New York, NY: ACM.
Hannay, J. E., Dybå, T., Arisholm, E., & Sjøberg, D. I. K. (2009). The effectiveness of pair programming: A meta-analysis. Information and Software Technology, 51(7), 1110–1122.
Harman, M., McMinn, P., de Souza, J. T., & Yoo, S. (2012). Search based software engineering: Techniques, taxonomy, tutorial. Empirical software engineering and verification (pp. 1–59). Berlin/Heidelberg: Springer.
Haynes, R. B., Wilczynski, K. A., McKibbon, C. J., & Sinclair, J. C. (1994). Developing optimal search strategies for detecting clinically sound studies in medline. Journal of the American Medical Informatics Association, 1, 447–458.
Kitchenham, B. (2011). Chapter three – What we can learn from systematic reviews. In Making software what really works, and why we believe it (Vol. 1, 1st ed.). Gravenstein Highway North, Sebastopol, CA: O’Reilly Media
Kitchenham, B., Mendes, E., & Travassos, G. H. (2007). A systematic review of cross vs. within-company cost estimation studies. IEEE Transactions on Software Engineering, 33(5), 361–329.
Kitchenham, B. A., Budgen, D., & Pearl Brereton, O. (2011). Using mapping studies as the basis for further research – A participant-observer case study. Information and Software Technology, 53(6), 638–651.
Kitchenham, B. A., Dyba, T., & Jorgensen, M. (2004). Evidence-based software engineering. In ICSE 2004, ICSE ’04, Edinburgh (pp. 273–281). Washington, DC: IEEE Computer Society.
Russell, S. J., & Norvig, P. (2003). Artificial intelligence: A modern approach (2nd ed.). Upper Saddle River, N.J: Pearson Education.
Straus, S., & Richardson, W. (2010). Evidence-based medicine: How to practice and teach it (4th ed.). Edinburgh: Churchill Livingstone.
Zhang, H., Babar, M. A., & Tell, P. (2011). Identifying relevant studies in software engineering. Information and Software Technology, 53(6), 625–637.
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Souza, F.C., Santos, A., Andrade, S., Durelli, R., Durelli, V., Oliveira, R. (2018). Automating Search Strings for Secondary Studies. In: Latifi, S. (eds) Information Technology - New Generations. Advances in Intelligent Systems and Computing, vol 558. Springer, Cham. https://doi.org/10.1007/978-3-319-54978-1_104
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