On the correlation between size and metric validity

Gil, Yossi; Lalouche, Gal

doi:10.1007/s10664-017-9513-5

Cited by 46 publications

(74 citation statements)

References 22 publications

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“…Thus, the likelihood of a class to change depends more on the content of the requirement than on class attributes such as size. This result is in contrast to a recent previous study showing that "size is the only unique metric" for software prediction [121]. 5) Distribution scores, among the last ten requirements touching the class, and NLP techniques of R2RS metrics slightly differ in IGR and selection proportion.…”

Section: Resultscontrasting

confidence: 99%

Leveraging Historical Associations between Requirements and Source Code to Identify Impacted Classes

Falessi

Roll

Guo

et al. 2020

IIEEE Trans. Software Eng.

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As new requirements are introduced and implemented in a software system, developers must identify the set of source code classes which need to be changed. Therefore, past effort has focused on predicting the set of classes impacted by a requirement. In this paper, we introduce and evaluate a new type of information based on the intuition that the set of requirements which are associated with historical changes to a specific class are likely to exhibit semantic similarity to new requirements which impact that class. This new Requirements to Requirements Set (R2RS) family of metrics captures the semantic similarity between a new requirement and the set of existing requirements previously associated with a class. The aim of this paper is to present and evaluate the usefulness of R2RS metrics in predicting the set of classes impacted by a requirement. We consider 18 different R2RS metrics by combining six natural language processing techniques to measure the semantic similarity among texts (e.g., VSM) and three distribution scores to compute overall similarity (e.g., average among similarity scores). We evaluate if R2RS is useful for predicting impacted classes in combination and against four other families of metrics that are based upon temporal locality of changes, direct similarity to code, complexity metrics, and code smells. Our evaluation features five classifiers and 78 releases belonging to four large open-source projects, which result in over 700,000 candidate impacted classes. Experimental results show that leveraging R2RS information increases the accuracy of predicting impacted classes practically by an average of more than 60% across the various classifiers and projects.

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Section: Resultscontrasting

confidence: 99%

Leveraging Historical Associations between Requirements and Source Code to Identify Impacted Classes

Falessi

Roll

Guo

et al. 2020

IIEEE Trans. Software Eng.

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“…Zhou et al (2009) have reported that size metrics have confounding effects on the associations between object-oriented metrics and change-proneness. On a revisited study, Gil and Lalouche (2017) reported similar results about the confounding of the size metric. Zhou et al (2009) have elaborately explained the confounding effect and models to identify them in areas like health sciences and epidemiological research.…”

Section: Related Workmentioning

confidence: 66%

Effects of measurements on correlations of software code metrics

2019

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Context Software metrics play a significant role in many areas in the life-cycle of software including forecasting defects and foretelling stories regarding maintenance, cost, etc. through predictive analysis. Many studies have found code metrics correlated to each other at such a high level that such correlated code metrics are considered redundant, which implies it is enough to keep track of a single metric from a list of highly correlated metrics. Objective Software is developed incrementally over a period. Traditionally, code metrics are measured cumulatively as cumulative sum or running sum. When a code metric is measured based on the values from individual revisions or commits without consolidating values from past revisions, indicating the natural development of software, this study identifies such a type of measure as organic. Density and average are two other ways of measuring metrics. This empirical study focuses on whether measurement types influence correlations of code metrics. Method To investigate the objective, this empirical study has collected 24 code metrics classified into four categories, according to the measurement types of the metrics, from 11,874 software revisions (i.e., commits) of 21 open source projects from eight well-known organizations. Kendall's τ-B is used for computing correlations. To determine whether there is a significant difference between cumulative and organic metrics, Mann-Whitney U test, Wilcoxon signed rank test, and paired-samples sign test are performed. Results The cumulative metrics are found to be highly correlated to each other with an average coefficient of 0.79. For corresponding organic metrics, it is 0.49. When individual correlation coefficients between these two measure types are compared, correlations between organic metrics are found to be significantly lower (with p <0.01) than cumulative metrics. Our results indicate that the cumulative nature of metrics makes them highly correlated, implying cumulative measurement is a major source of collinearity between cumulative metrics. Another interesting observation is that correlations between metrics from different categories are weak.

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“…Lalouche [58] On the correlation between size and metric validity Sample Study Presents an analysis of a set of 26 metrics and a dataset of over 53,000 Java source code files that demonstrates that the validity of metrics depends on their correlation with size.…”

Section: Gil Andmentioning

confidence: 99%

The ABC of Software Engineering Research

Stol

Fitzgerald

2018

ACM Trans. Softw. Eng. Methodol.

156

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A variety of research methods and techniques are available to SE researchers, and while several overviews exist, there is consistency neither in the research methods covered nor in the terminology used. Furthermore, research is sometimes critically reviewed for characteristics inherent to the methods. We adopt a taxonomy from the social sciences, termed here the ABC framework for SE research, which offers a holistic view of eight archetypal research strategies. ABC refers to the research goal that strives for generalizability over Actors (A) and precise measurement of their Behavior (B), in a realistic Context (C). The ABC framework uses two dimensions widely considered to be key in research design: the level of obtrusiveness of the research and the generalizability of research findings. We discuss metaphors for each strategy and their inherent limitations and potential strengths. We illustrate these research strategies in two key SE domains, global software engineering and requirements engineering, and apply the framework on a sample of 75 articles. Finally, we discuss six ways in which the framework can advance SE research.

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On the correlation between size and metric validity

Cited by 46 publications

References 22 publications

Leveraging Historical Associations between Requirements and Source Code to Identify Impacted Classes

Leveraging Historical Associations between Requirements and Source Code to Identify Impacted Classes

Effects of measurements on correlations of software code metrics

The ABC of Software Engineering Research

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