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Weighing lexical information for software clustering in the context of architecture recovery

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Abstract

In this paper, we present a software clustering approach that leverages the information conveyed by the zone in which each lexeme appears in the classes of object oriented systems. We define six zones in the source code: Class Name, Attribute Name, Method Name, Parameter Name, Comment, and Source Code Statement. These zones may convey information with different levels of relevance, and so their contribution should be differently weighed according to the software system under study. To this aim, we define a probabilistic model of the lexemes distribution whose parameters are automatically estimated by the Expectation-Maximization algorithm. The weights of the zones are then exploited to compute similarities among source code classes, which are then grouped by a k-Medoid clustering algorithm. To assess the validity of our solution in the software architecture recovery field, we applied our approach to 19 software systems from different application domains. We observed that the use of our probabilistic model and the defined zones improves the quality of clustering results so that they are close to a theoretical upper bound we have proved.

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Notes

  1. It is equal to the probability of the observed data x given the values of the model parameters 𝜃, i.e., \(\mathcal {L} (\theta | x) = P (x|\theta )\).

  2. A term is a normalized type that is included in the dictionary. A type is the class of all the tokens containing the same character sequence (Manning et al. 2008).

  3. http://www2.unibas.it/gscanniello/software-clustering/

  4. The complete formulation is reported in Saw et al. (1984).

  5. Given two values a and b the means percentage improvement is computed as \(\frac {(b-a)}{a}\).

  6. Each zone has a different vocabulary, so if the same lexeme appears in two or more zones we considered different the terms in all the zones where the lexeme appears.

  7. The Shapiro-Wilk W test returned 0.049 and 0.063 as the p-values for VSM and ZU, respectively. This confirms our postulation on the distribution of data.

  8. The Shapiro-Wilk W test returned 0.063 for ZU and 0.04 for ZWB. On the other hand, this test returned 0.098 for ZWG. Although the results Shapiro-Wilk W test suggest that a parametric test (e.g., unpaired t-test) could be used to verify the presence of a statistically significant difference between the values of authoritativeness obtained with ZWG and ZU, we used the Mann-Whitney test because repeated statistical tests were needed to test Hn2.

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Authors and Affiliations

  1. Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Napoli, Italy

    Anna Corazza & Sergio Di Martino

  2. Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Salerno, Italy

    Valerio Maggio

  3. Dipartimento di Matematica, Informatica e Economia, University of Basilicata, Potenza, Italy

    Giuseppe Scanniello

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  1. Anna Corazza
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  2. Sergio Di Martino
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  4. Giuseppe Scanniello
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Correspondence to Valerio Maggio.

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Communicated by: Thomas Zimmermann

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Corazza, A., Di Martino, S., Maggio, V. et al. Weighing lexical information for software clustering in the context of architecture recovery. Empir Software Eng 21, 72–103 (2016). https://doi.org/10.1007/s10664-014-9347-3

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