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Experimenting with information retrieval methods in the recovery of feature-code SPL traces

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Abstract

Context

The information retrieval research provides alternatives to recover traces from existing software information. In the software product line (SPL) engineering context, further research on information retrieval methods is required to explore the existence of products’ source code and support the SPL adoption by providing traceability information.

Objective

This work evaluates the methods’ retrieval quality targeting the extraction of feature-code trace links in a set of variant-rich software development projects.

Method

We propose a research method comprising two experiments with five information retrieval methods (Classic vector, Latent semantic indexing, Neural network, Extended boolean and BM25), applied to forty-one projects. The SPLTrac suite automates the research operation, using the information retrieval methods results as input to perform a five-step quantitative data analysis procedure based on parametric and non-parametric statistical techniques. The quality measurement is expressed by four dependent variables: precision, recall, F-measure and execution time.

Results

With a homogeneous result for execution time between methods, there are discrepancies for the other metrics. While Extended boolean presents the best results for precision and F-measure, BM25 provides the greatest recall results.

Conclusions

Such evidence indicates there is not a dominant method in terms of retrieval quality, since they need further improvements to achieve better performance in all quality perspectives.

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Notes

  1. Antenna variability implementation technology - http://antenna.sourceforge.net/

  2. The SPL2Go repository is http://spl2go.cs.ovgu.de/

  3. The Freecode index is http://freecode.com/

  4. Relevant traces extractor module - https://github.com/tassiovale/spltrac/tree/1.3.0/features_extraction

  5. https://www.python.org/

  6. SPLTrac repository - https://github.com/tassiovale/spltrac

  7. R project for statistical computing - https://www.r-project.org/

  8. SPL metrics results https://goo.gl/CgUp9G

  9. SPL hypothesis testing results: https://goo.gl/a3kY1C

  10. SPL correlation results: https://goo.gl/U4SMcU

  11. Regression analysis results: https://goo.gl/W3qoYS

  12. Preprocessor metrics results: https://goo.gl/rtkbWB

  13. Preprocessor hypothesis testing results: https://goo.gl/LNACyd

  14. Preprocessor correlation results: https://goo.gl/3aJxX2

  15. Comparison results: https://goo.gl/LFknPx

  16. Effect size estimation results: https://goo.gl/n3Jr7N

  17. NLTK - https://www.nltk.org/

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Author information

Authors and Affiliations

  1. Center of Exact Sciences and Technology, Federal University of Recôncavo da Bahia, Rua Rui Barbosa, 710, Centro Cruz das Almas, Salvador, BA, Brazil

    Tassio Vale

  2. Computer Science Department, Federal University of Bahia, Av. Adhemar de Barros, S/N, Ondina, Salvador, BA, Brazil

    Eduardo Santana de Almeida

Authors
  1. Tassio Vale
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  2. Eduardo Santana de Almeida
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Correspondence to Tassio Vale.

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Communicated by: Paul Grünbacher

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Appendix A: Statistical Results

Appendix A: Statistical Results

1.1 A.1 SPL2Go Dataset Results

Table 8 SPL context - precision results (%)
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Table 9 SPL context - recall results (%)
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Table 10 SPL context - F-measure results (%)
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Table 11 SPL context - execution time results (in seconds)
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1.2 A.2 Preprocessor Dataset Results

Table 12 Preprocessor context - precision results (%)
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Table 13 Preprocessor context - recall results (%)
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Table 14 Preprocessor context - F-measure results (%)
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Table 15 Preprocessor context - execution time results (in seconds)
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1.3 A.3 Normality Test

Table 16 Shapiro-Wilk normality test results
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1.4 A.4 Hypothesis Testing

Table 17 ANOVA results
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Table 18 Kruskal-Wallis results
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Table 19 SPL context - Welch’s t results
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Table 20 F-measure in the SPL context - Wilcoxon results
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Table 21 Preprocessor context - Welch’s t results
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1.5 A.5 Strength of Association Between Variables

Table 22 Strong correlation per method sample - pearson coefficient
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1.6 A.6 Cross-Sample Analysis

Table 23 Welch’s t results
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1.7 A.7 Effect Size Estimation

Table 24 Hedge’s g large effects
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Vale, T., de Almeida, E. Experimenting with information retrieval methods in the recovery of feature-code SPL traces. Empir Software Eng 24, 1328–1368 (2019). https://doi.org/10.1007/s10664-018-9652-3

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