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Assessment and cross-product prediction of software product line quality: accounting for reuse across products, over multiple releases

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Abstract

The goals of cross-product reuse in a software product line (SPL) are to mitigate production costs and improve the quality. In addition to reuse across products, due to the evolutionary development process, a SPL also exhibits reuse across releases. In this paper, we empirically explore how the two types of reuse—reuse across products and reuse across releases—affect the quality of a SPL and our ability to accurately predict fault proneness. We measure the quality in terms of post-release faults and consider different levels of reuse across products (i.e., common, high-reuse variation, low-reuse variation, and single-use packages), over multiple releases. Assessment results showed that quality improved for common, low-reuse variation, and single-use packages as they evolved across releases. Surprisingly, within each release, among preexisting (‘old’) packages, the cross-product reuse did not affect the change and fault proneness. Cross-product predictions based on pre-release data accurately ranked the packages according to their post-release faults and predicted the 20 % most faulty packages. The predictions benefited from data available for other products in the product line, with models producing better results (1) when making predictions on smaller products (consisting mostly of common packages) rather than on larger products and (2) when trained on larger products rather than on smaller products.

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Notes

  1. A fault is defined as an accidental condition, which if encountered, may cause the system or system component to fail to perform as required. We avoid using the term defect, which is used inconsistently in the literature to refer in some cases to both faults and failures and in other cases only to faults or perhaps, faults detected pre-release.

  2. For a comprehensive survey of binary classification studies the reader is referred to the recent paper by Hall et al. (2012).

  3. For this study, a package is considered faulty if any file contained in that package exhibited one or more post-release faults.

  4. Thompson and Heimdahl (2003) proposed a set-theoretic approach to represent requirements reuse in product line engineering, which described the boundaries of sets as commonalities and the members within the sets as products. The approach taken in our previous work (Devine et al. 2012) and used here is complementary to Thompson and Heimdahl (2003). Specifically, it is used to illustrate the amount of shared code at different levels of cross-product reuse; the elements within the sets are packages of the SPL, and the boundaries of sets define the products.

  5. pserver:anonymous@dev.eclipse.org:2401/cvsroot

  6. The complexity measure used by SourceMonitor approximately follows the definition by McConnell (2004).

  7. Some form of \(k\)-fold cross validation is commonly employed in machine learning in general and software engineering in particular. Cross validation is the process of splitting the data randomly into \(k\) groups, and then predicting values for the \(k\)-th group by building a model on the other \(k-1\) groups. This is repeated using each of the \(k\) groups as a testing group and the average value of the predicted variable is reported. Cross validation may provide better results than building models and predicting on disjoint data sets (as was done in this paper) because averaging the results over \(k\) repeated trials offers more consistent, flattened end results than one achieved via building models and predicting on disjoint sets.

  8. Many software metrics are highly correlated to each other, which engenders a problem that is commonly referred to as multicollinearity. To quote Kutner et al. (2004) “The fact that some or all predictor variables are correlated among themselves does not, in general, inhibit our ability to obtain a good fit nor does it tend to affect inferences about mean responses or predictions of new observations ...” However, multicollinearity may cause the estimated regression coefficients to have a large sampling variability and thus affect explanatory studies.

  9. Kendall’s \(\tau _b\) approaches the normal distribution quite rapidly so that the normal approximation is better for Kendall’s \(\tau _b\) than it is for Spearman’s \(\rho \). Another advantage of Kendall’s \(\tau _b\) is its direct and simple interpretation in terms of probabilities of observing concordant pairs (both numbers of one observation are larger than their respective members of the other observation) and discordant pairs (the two numbers in one observation differ in opposite directions from the respective members in the other observation).

  10. If the Friedman test results in rejection of the null hypothesis that there is no difference, a post hoc multiple comparison test is used to identify where the difference is. Alternatively, instead of the Friedman test, one can use the Page test which is used to test the null hypothesis that there is no statistically significant difference in several related samples (i.e., \(H_0: \mu _1 = \mu _2 = \mu _3\)) against the ordered alternative that the samples differ in a specified direction, with at least one inequality (i.e., \(H_1: \mu _1 \ge \mu _2 \ge \mu _3\)).

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Acknowledgments

This work was supported in part by the National Science Foundation Grants 0916275 and 0916284 with funds from the American Recovery and Reinvestment Act of 2009 and by the WVU ADVANCE Sponsorship Program funded by the National Science Foundation ADVANCE IT Program award HRD-100797. Part of this work was performed while Robyn Lutz was visiting the California Institute of Technology.

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Authors and Affiliations

  1. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

    Thomas Devine & Katerina Goseva-Popstojanova

  2. Department of Computer Science, Iowa State University, Ames, IA, USA

    Sandeep Krishnan & Robyn R. Lutz

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  1. Thomas Devine
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  2. Katerina Goseva-Popstojanova
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  3. Sandeep Krishnan
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  4. Robyn R. Lutz
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Corresponding author

Correspondence to Katerina Goseva-Popstojanova.

Appendix: Aggregation metrics

Appendix: Aggregation metrics

The static code and change metrics were collected at file-level and then were aggregated to the package level, as specified in Tables 7 and 8. As a result, each package was characterized by a vector \(\mathbf{m}\) of 112 metrics (i.e., features), where \(\mathbf{m}\left[ i \right] , i=1,\ldots ,73\) are static code metrics, while \(\mathbf{m}\left[ i \right] , i=74,\ldots ,112\) are change metrics.

Table 7 Aggregations applied to each static code metric
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Table 8 Aggregations applied to each change metric
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Devine, T., Goseva-Popstojanova, K., Krishnan, S. et al. Assessment and cross-product prediction of software product line quality: accounting for reuse across products, over multiple releases. Autom Softw Eng 23, 253–302 (2016). https://doi.org/10.1007/s10515-014-0160-4

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