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Global vs. local models for cross-project defect prediction

A replication study

  • Published:
Empirical Software Engineering Aims and scope Submit manuscript

Abstract

Although researchers invested significant effort, the performance of defect prediction in a cross-project setting, i.e., with data that does not come from the same project, is still unsatisfactory. A recent proposal for the improvement of defect prediction is using local models. With local models, the available data is first clustered into homogeneous regions and afterwards separate classifiers are trained for each homogeneous region. Since the main problem of cross-project defect prediction is data heterogeneity, the idea of local models is promising. Therefore, we perform a conceptual replication of the previous studies on local models with a focus on cross-project defect prediction. In a large case study, we evaluate the performance of local models and investigate their advantages and drawbacks for cross-project predictions. To this aim, we also compare the performance with a global model and a transfer learning technique designed for cross-project defect predictions. Our findings show that local models make only a minor difference in comparison to global models and transfer learning for cross-project defect prediction. While these results are negative, they provide valuable knowledge about the limitations of local models and increase the validity of previously gained research results.

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Notes

  1. With the data used in the study and the success criterion of having both recall and precision of at least 0.75 they achieved a success rate of about 3 %.

  2. The studies by Menzies et al and Bettenburg et al were first published in an initial version at a conference and then in greater detail in a journal publication, leading to five publications for the three studies.

  3. recall, precision, and accuracy all at least 0.75.

  4. The tera-PROMISE repository is the successor of the PROMISE repository, which was previously located at http://promisedata.googlecode.com.

  5. http://bug.inf.usi.ch/

  6. instead of recall, sometimes PD or tpr are used in the literature. PD stands for probability of defect and tpr for true positive rate.

  7. This problem is still very relevant. For example, during the 37th International Conference on Software Engineering held in May 2015, there were five papers on defect prediction (Caglayan et al. 2015; Ghotra et al. 2015; Peters et al. 2015; Tan et al. 2015; Tantithamthavorn et al. 2015). None of them used exactly the same performance measures.

  8. Github: https://github.com/sherbold/replication-kit-emse-2016-local-models/tree/master/replication-kit Zipped Archive: http://hdl.handle.net/21.11101/0000-0001-3C55-D

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Authors and Affiliations

  1. Institute of Computer Science, Georg-August-Universität Göttingen, Göttingen, Germany

    Steffen Herbold, Alexander Trautsch & Jens Grabowski

Authors
  1. Steffen Herbold
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  2. Alexander Trautsch
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  3. Jens Grabowski
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Corresponding author

Correspondence to Steffen Herbold.

Additional information

Communicated by: Burak Turhan

Appendix A: Metrics

Appendix A: Metrics

1.1 A.1 JSTAT Data

The following metrics are part of the JSTAT data:

  • WMC: weighted method count, number of methods in a class

  • DIT: depth of inheritance tree

  • NOC: number of children

  • CBO: coupling between objects, number of classes coupled to a class

  • RFC: response for class, number of different methods that can be executed if the class receives a message

  • LCOM: lack of cohesion in methods, number of methods not related through the sharing of some of the class fields

  • LCOM3: lack of cohesion in methods after Henderson-Sellers (1996)

  • NPM: number of public methods

  • DAM: data access metric, ratio of private (protected) attributes to total number of attributes in the class

  • MOA: measure of aggregation, number of class fields whose types are user defined classes

  • MFA: measure of functional abstraction, ratio of the number of methods inherited by a class to the total number of methods accessible by the member methods of the class

  • CAM: cohesion among methods of class, relatedness of methods based upon the parameter list of the methods

  • IC: inheritance coupling, number of parent classes to which the class is coupled

  • CBM: coupling between methods, number of new/redefined methods to which all the inherited methods are coupled

  • AMC: average method complexity

  • Ca: afferent couplings

  • Ce: efferent couplings

  • CC: cyclomatic complexity

  • Max(CC): maximum cyclomatic complexity among methods

  • Avg(CC): average cyclomatic complexity among methods

For a detailed explanation see Jureczko and Madeyski (2010).

1.2 A.2 MDP Data

The following metrics are part of the MDP data. This is the common subset of metrics that is obtained by all projects within the MDP data set:

  • LOC_TOTAL: total lines of code

  • LOC_EXECUTABLE: exectuable lines of code

  • LOC_COMMENTS: lines of comments

  • LOC_CODE_AND_COMMENT: lines with comments or code

  • NUM_UNIQUE_OPERATORS: number of unique operators

  • NUM_UNIQUE_OPERANDS: number of unique operands

  • NUM_OPERATORS: total number of operators

  • NUM_OPERANDS: total number of operands

  • HALSTEAD_VOLUME: Halstead volume (see Halstead 1977)

  • HALSTEAD_LENGTH: Halstead length (see Halstead 1977)

  • HALSTEAD_DIFFICULTY: Halstead difficulty (see Halstead 1977)

  • HALSTEAD_EFFORT: Halstead effort (see Halstead 1977)

  • HALSTEAD_ERROR_EST: Halstead Error, also known as Halstead Bug (see Halstead 1977)

  • HALSTEAD_PROG_TIME: Halstead Pro

  • BRANCH_COUNT: Number of branches

  • CYCLOMATIC_COMPLEXITY: Cyclomatic complexity (same as CC in the JSTAT data)

  • DESIGN_COMPLEXITY: design complexity

1.3 A.3 JPROC Data

The following metrics are part of the JPROC data:

  • CBO: coupling between objects

  • DIT: depth of inheritance tree

  • fanIn: number of other classes that reference the class

  • fanOut: number of other classes referenced by the class

  • LCOM: lack of cohesion in methods

  • NOC: number of children

  • RFC: response for class

  • WMC: weighted method count

  • NOA: number of attributes

  • NOAI: number of attributes inherited

  • LOC: lines of code

  • NOM: number of methods

  • NOMI: number of methods inherited

  • NOPRA: number of private attributes

  • NOPRM: number of private methods

  • NOPA: number of public attributes

  • NOPM: number of public methods

  • NR: number of revisions

  • NREF: number of times the file has been refactored

  • NAUTH: number of authors

  • LADD: sum of lines added

  • max(LADD): maximum lines added

  • avg(LADD): average lines added

  • LDEL: sum of lines removed

  • max(LDEL): maximum lines deleted

  • avg(LDEL): average lines deleted

  • CHURN: sum of code churn

  • max(CHURN): maximum code churn

  • avg(CHURN): average code churn

  • AGE: age of the file

  • WAGE: weighted age of the file

For a detailed explanation see D’Ambros et al. (2010).

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Herbold, S., Trautsch, A. & Grabowski, J. Global vs. local models for cross-project defect prediction. Empir Software Eng 22, 1866–1902 (2017). https://doi.org/10.1007/s10664-016-9468-y

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