Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing studies empirically confirmed that the presence of code smells increases the likelihood of subsequent changes (i.e., change-proness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smellbased metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort

Estimation of Defect proneness Using Design complexity Measurements in Object- Oriented Software

Software engineering is continuously facing the challenges of growing complexity of software packages and increased level of data on defects and drawbacks from software production process. This makes a clarion call for inventions and methods which can enable a more reusable, reliable, easily maintainable and high quality software systems with deeper control on software generation process. Quality and productivity are indeed the two most important parameters for controlling any industrial process. Implementation of a successful control system requires some means of measurement. Software metrics play an important role in the management aspects of the software development process such as better planning, assessment of improvements, resource allocation and reduction of unpredictability. The process involving early detection of potential problems, productivity evaluation and evaluating external quality factors such as reusability, maintainability, defect proneness and complexity are of utmost importance. Here we discuss the application of CK metrics and estimation model to predict the external quality parameters for optimizing the design process and production process for desired levels of quality. Estimation of defect-proneness in object-oriented system at design level is developed using a novel methodology where models of relationship between CK metrics and defect-proneness index is achieved. A multifunctional estimation approach captures the correlation between CK metrics and defect proneness level of software modules.Comment: 5 pages, 1 figur