Classes In Object-oriented Systems Research Articles

Key Classes in Object-Oriented Systems: Detection and Assessment

Inadequate documentation of software design has been known to be a barrier for developers. Interestingly, several relevant object-oriented systems have their design documented using key classes, which are meant to represent key concepts of the systems. In order to fill the gap of under-documented design, we present Keecle, an approach for detecting a predefined number of key classes in a semi-automatic way. The main challenge is to reduce the space of potentially thousands of classes to just a few representatives of the main concepts of a system, while maintaining high precision. The approach is evaluated with 13 systems in order to assess its correctness. The ground-truth is obtained either from the original documentation, or from third-party, or from the respective developers. The results were analyzed in terms of precision and recall, and have shown to be superior compared to the state-of-the-art approach. In order to evaluate if key classes are more critical from the design point of view, we evaluated whether they are associated with cohesion and coupling metrics. We found that although key classes, in general, are critical from the point of view of design, there are other classes that are also critical, suggesting that being aware of key classes encompass information not available in structural metrics, and could be useful as a additional facet for design assessment.

Predicting Unit Testing Effort Levels of Classes: An Exploratory Study based on Multinomial Logistic Regression Modeling

The study aims at investigating empirically the ability of a Quality Assurance Indicator (Qi), a metric that we proposed in a previous work, to predict different levels of unit testing effort of classes in object-oriented systems. To capture the unit testing effort of classes, we used four metrics to quantify various perspectives related to the code of corresponding unit test cases. Classes were classified, according to the involved unit testing effort, in five categories (levels). We collected data from two open source Java software systems (ANT and JFREECHART) for which JUnit test cases exist. In order to explore the ability of the Qi metric to predict different levels of the unit testing effort of classes, we decided to explore the possibility of using the Multinomial Logistic Regression (MLR) method. The performance of the Qi metric has been compared to the performance of three well-known source code metrics related respectively to size, complexity and coupling. Results suggest that the MLR model based on the Qi metric is able to accurately predict different levels of the unit testing effort of classes.

Understanding Change Prone Classes in Object Oriented Software

Classes in Object Oriented Systems are continuously subjected to changes and defect prone. Predicting such classes is a key research area in the field of software engineering. It is important to identify such change prone classes and defect prone classes. Identifying change prone classes can help developers to build quality software on time. Considering all the above issues, this paper covers the following key issues: 1) identification of change prone classes using various approaches 2) How changes in one class affects multiple classes associated with it. 3) Study Dependency between classes and their effects.

On the relationship of class stability and maintainability

Maintainability is an essential software quality attribute as software maintenance is a costly process. ISO 9126 characterised maintainability with five sub-characteristics, one of which is stability. Unstable software may lead to high maintenance cost and effort. Classes in object-oriented systems form the basic elements of the software architecture; hence, stable classes may contribute to reducing the software maintenance cost and effort. In this study, the author conducts an empirical study to evaluate the relationship between class stability and maintainability. The author correlates class stability with maintainability effort measured by the number of hours spent on maintenance activities and by the line of code changes. Results show that classes with higher values of stability measured by the class stability metric (CSM) are associated with a lower value of perfective maintenance effort measured by hours. CSM also correlated with all types of maintenance (corrective, adaptive and perfective) if measured for the cumulatively combined system classes in all iterations rather than per iteration. The author also found that none of the stability metrics show a relationship with maintainability when measured by number of line of code changes.

Evaluating the Effect of Control Flow on the Unit Testing Effort of Classes: An Empirical Analysis

The aim of this paper is to evaluate empirically the relationship between a new metric (Quality Assurance Indicator—Qi) and testability of classes in object-oriented systems. The Qi metric captures the distribution of the control flow in a system. We addressed testability from the perspective of unit testing effort. We collected data from five open source Java software systems for which JUnit test cases exist. To capture the testing effort of classes, we used different metrics to quantify the corresponding JUnit test cases. Classes were classified, according to the required testing effort, in two categories: high and low. In order to evaluate the capability of the Qi metric to predict testability of classes, we used the univariate logistic regression method. The performance of the predicted model was evaluated using Receiver Operating Characteristic (ROC) analysis. The results indicate that the univariate model based on the Qi metric is able to accurately predict the unit testing effort of classes.

Improving Class Cohesion Measurement: Towards a Novel Approach Using Hierarchical Clustering

Class cohesion is considered as one of the most important object-oriented software attributes. High cohesion is, in fact, a desirable property of software. Many different metrics have been suggested in the last several years to measure the cohesion of classes in object-oriented systems. The class of structural object-oriented cohesion metrics is the most in-vestigated category of cohesion metrics. These metrics measure cohesion on structural information extracted from the source code. Empirical studies noted that these metrics fail in many situations to properly reflect cohesion of classes. This paper aims at exploring the use of hierarchical clustering techniques to improve the measurement of cohesion of classes in object-oriented systems. The proposed approach has been evaluated using three particular case studies. We also used in our study three well-known structural cohesion metrics. The achieved results show that the new approach appears to better reflect the cohesion (and structure) of classes than traditional structural cohesion metrics.

An exploratory study of the impact of antipatterns on class change- and fault-proneness

Antipatterns are poor design choices that are conjectured to make object-oriented systems harder to maintain. We investigate the impact of antipatterns on classes in object-oriented systems by studying the relation between the presence of antipatterns and the change- and fault-proneness of the classes. We detect 13 antipatterns in 54 releases of ArgoUML, Eclipse, Mylyn, and Rhino, and analyse (1) to what extent classes participating in antipatterns have higher odds to change or to be subject to fault-fixing than other classes, (2) to what extent these odds (if higher) are due to the sizes of the classes or to the presence of antipatterns, and (3) what kinds of changes affect classes participating in antipatterns. We show that, in almost all releases of the four systems, classes participating in antipatterns are more change-and fault-prone than others. We also show that size alone cannot explain the higher odds of classes with antipatterns to underwent a (fault-fixing) change than other classes. Finally, we show that structural changes affect more classes with antipatterns than others. We provide qualitative explanations of the increase of change- and fault-proneness in classes participating in antipatterns using release notes and bug reports. The obtained results justify a posteriori previous work on the specification and detection of antipatterns and could help to better focus quality assurance and testing activities.

On the ability of complexity metrics to predict fault-prone classes in object-oriented systems

Many studies use logistic regression models to investigate the ability of complexity metrics to predict fault-prone classes. However, it is not uncommon to see the inappropriate use of performance indictors such as odds ratio in previous studies. In particular, a recent study by Olague et al. uses the odds ratio associated with one unit increase in a metric to compare the relative magnitude of the associations between individual metrics and fault-proneness. In addition, the percents of concordant, discordant, and tied pairs are used to evaluate the predictive effectiveness of a univariate logistic regression model. Their results suggest that lesser known complexity metrics such as standard deviation method complexity (SDMC) and average method complexity (AMC) are better predictors than the two commonly used metrics: lines of code (LOC) and weighted method McCabe complexity (WMC). In this paper, however, we show that (1) the odds ratio associated with one standard deviation increase, rather than one unit increase, in a metric should be used to compare the relative magnitudes of the effects of individual metrics on fault-proneness. Otherwise, misleading results may be obtained; and that (2) the connection of the percents of concordant, discordant, and tied pairs with the predictive effectiveness of a univariate logistic regression model is false, as they indeed do not depend on the model. Furthermore, we use the data collected from three versions of Eclipse to re-examine the ability of complexity metrics to predict fault-proneness. Our experimental results reveal that: (1) many metrics exhibit moderate or almost moderate ability in discriminating between fault-prone and not fault-prone classes; (2) LOC and WMC are indeed better fault-proneness predictors than SDMC and AMC; and (3) the explanatory power of other complexity metrics in addition to LOC is limited.

An empirical study of the bad smells and class error probability in the post-release object-oriented system evolution

Bad smells are used as a means to identify problematic classes in object-oriented systems for refactoring. The belief that the bad smells are linked with problematic classes is largely based on previous metric research results. Although there is a plethora of empirical studies linking software metrics to errors and error proneness of classes in object-oriented systems, the link between the bad smells and class error probability in the evolution of object-oriented systems after the systems are released has not been explored. There has been no empirical evidence linking the bad smells with class error probability so far. This paper presents the results from an empirical study that investigated the relationship between the bad smells and class error probability in three error-severity levels in an industrial-strength open source system. Our research, which was conducted in the context of the post-release system evolution process, showed that some bad smells were positively associated with the class error probability in the three error-severity levels. This finding supports the use of bad smells as a systematic method to identify and refactor problematic classes in this specific context.