At ICPC 2010 we presented an empirical study to statistically analyze the equivalence of several traceability recovery methods based on Information Retrieval (IR) techniques [1]. We experimented the Vector Space Model (VSM) [2], Latent Semantic Indexing (LSI) [3], the Jensen-Shannon (JS) method [4], and Latent Dirichlet Allocation (LDA) [5]. Unlike previous empirical studies we did not compare the different IR based traceability recovery methods only using the usual precision and recall metrics. We introduced some metrics to analyze the overlap of the set of candidate links recovered by each method. We also based our analysis on Principal Component Analysis (PCA) to analyze the orthogonality of the experimented methods. The results showed that while the accuracy of LDA was lower than previously used methods, LDA was able to capture some information missed by the other exploited IR methods. Instead, JS, VSM, and LSI were almost equivalent. This paved the way to possible integration of IR based traceability recovery methods [6]. Our paper was one of the first papers experimenting LDA for traceability recovery. Also, the overlap metrics and PCA have been used later to compare and possibly integrate different recommendation approaches not only for traceability recovery, but also for other reverse engineering and software maintenance tasks, such as code smell detection, design pattern detection, and bug prediction.

[1] R. Oliveto, M. Gethers, D. Poshyvanyk, A. De Lucia, “On the Equivalence of Information Retrieval Methods for Automated Traceability Link Recovery”, in Proc. of the International Conference on Program Comprehension, pp. 68-71, 2010.

[2] R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley, 1999.

[3] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “Indexing by Latent Semantic Analysis”, Journal of the American Society for Information Science, vol. 41, n. 16, pp. 391?407, 1990.

[4] A. Abadi, M. Nisenson, and Y. Simionovici, “A Traceability Technique for Specifications”, in Proc. of the International Conference on Program Comprehension, pp. 103-112, 2008.

[5] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation”, The Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.

[6] M. Gethers, R. Oliveto, D. Poshyvanyk, A. De Lucia, “On Integrating Orthogonal Information Retrieval Methods to Improve Traceability Recovery”, in Proc. of the International Conference on Software Maintenance, pp. 133-142, 2011.