An Investigation of the Relationships between Software Metrics and Defects

Abstract

Open source software systems are becoming more popular today, and are playing important roles in many scientific and business software applications. Many companies are investing in open source projects and lots of them are also using such software in their own work. But, because open source software is often developed with a different management style than the industrial ones, the quality and reliability of the code needs to be investigated. Hence, more projects need to be measured to obtain information about the characteristics and nature of the source code.In this paper we have evaluated the object-oriented metrics given by Chidamber and Kemerer, and few other static code metrics for two open source projects. We have carried out an empirical study and tried to find out the nature of relationship between these metrics and defects. In other words, it has been investigated whether these metrics are significantly associated with defects or not. For this we have extracted source code, defects processed it for metrics .Two open source java projects of emulators have been taken from source forge and used for this purpose.

Inheritance software metrics on smart contracts

“…In OO programming, inheritance is a technique by which one class or object can reuse code from another class or object [23]. Due to the significance of inheritance in OO programming, its implication on cost, quality, and maintainability have been thoroughly examined [12,32,39,41,46]. A high degree of inheritance is often linked with greater complexity [10], which translates to more faults and higher costs.…”

Effect of Temperature & Supply Voltage Variation on Stability of 9T SRAM Cell at 45 nm Technology for Various Process Corners

“…The RD signal is always connected to ground reference during the read operation. The data storage in the 9T cell is performed by the crosscouple inverters [7]. Two NMOS access transistors (PG) NA1 and NA2 connect to the virtual storage nodes (V1 & V2) to the write bitline pair when the write wordline (WDL) is on.…”

Integrating software quality models into risk-based testing

“…Several contributions in the literature (i.e., Zhang (2009), Singh et al (2011)) state that complexity and size are important predictors for software defects; thus, we recommend a weight for 0.3 for the Complexity factor. A weight of 0.1 is assigned to both the Javadoc and PMD factor because they only provide two (Javadoc) and four (PMD) rules for analysis of the source code and have therefore not the expressiveness as the other two factors.…”

“…In the literature, several methods for analyzing relationships (e.g., logistic regression (Basili et al 1995) or Spearman rank correlation (Singh et al 2011)) were proposed. Due to the fact that the tool ranks the classes according to their risk coefficient, an appropriate way of answering research question RQ1 is Spearman's rank correlation coefficient (Spearman 1904).…”

“…The weights for the metrics of the Complexity factor are determined based on common literature on software complexity (i.e., Singh et al (2011), Zhang (2009 (2013), Krusko (2003), Basili et al (1995), Radjenovic et al (2013)). Metrics suggested by literature which are highly relevant for predicting defects were weighted higher than the others.…”