Rudolf Ferenc

Empirical validation of object-oriented metrics on open source software for fault prediction

Open source software systems are becoming increasingly important these days. 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 studied. Hence, the characteristics of the source code of these projects need to be measured to obtain more information about it. This paper describes how we calculated the object-oriented metrics given by Chidamber and Kemerer to illustrate how fault-proneness detection of the source code of the open source Web and e-mail suite called Mozilla can be carried out. We checked the values obtained against the number of bugs found in its bug database - called Bugzilla - using regression and machine learning methods to validate the usefulness of these metrics for fault-proneness prediction. We also compared the metrics of several versions of Mozilla to see how the predicted fault-proneness of the software system changed during its development cycle.

Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems

High cohesion is a desirable property of software as it positively impacts understanding, reuse, and maintenance. Currently proposed measures for cohesion in Object-Oriented (OO) software reflect particular interpretations of cohesion and capture different aspects of it. Existing approaches are largely based on using the structural information from the source code, such as attribute references, in methods to measure cohesion. This paper proposes a new measure for the cohesion of classes in OO software systems based on the analysis of the unstructured information embedded in the source code, such as comments and identifiers. The measure, named the Conceptual Cohesion of Classes (C3), is inspired by the mechanisms used to measure textual coherence in cognitive psychology and computational linguistics. This paper presents the principles and the technology that stand behind the C3 measure. A large case study on three open source software systems is presented which compares the new measure with an extensive set of existing metrics and uses them to construct models that predict software faults. The case study shows that the novel measure captures different aspects of class cohesion compared to any of the existing cohesion measures. In addition, combining C3 with existing structural cohesion metrics proves to be a better predictor of faulty classes when compared to different combinations of structural cohesion metrics.

Columbus - reverse engineering tool and schema for C++

One of the most critical issues in large-scale software development and maintenance is the rapidly growing size and complexity of software systems. As a result of this rapid growth there is a need to better understand the relationships between the different parts of a large software system. In this paper we present a reverse engineering framework called Columbus that is able to analyze large C++ projects, and a schema for C++ that prescribes the form of the extracted data. The flexible architecture of the Columbus system with a powerful C++ analyzer and schema makes it a versatile and readily extendible toolset for reverse engineering. This tool is free for scientific and educational purposes and we fervently hope that it will assist academic persons in any research work related to C++ re- and reverse engineering.

Using information retrieval based coupling measures for impact analysis

Coupling is an important property of software systems, which directly impacts program comprehension. In addition, the strength of coupling measured between modules in software is often used as a predictor of external software quality attributes such as changeability, ripple effects of changes and fault-proneness. This paper presents a new set of coupling measures for Object-Oriented (OO) software systems measuring conceptual coupling of classes. Conceptual coupling is based on measuring the degree to which the identifiers and comments from different classes relate to each other. This type of relationship, called conceptual coupling, is measured through the use of Information Retrieval (IR) techniques. The proposed measures are different from existing coupling measures and they capture new dimensions of coupling, which are not captured by the existing coupling measures. The paper investigates the use of the conceptual coupling measures during change impact analysis. The paper reports the findings of a case study in the source code of the Mozilla web browser, where the conceptual coupling metrics were compared to nine existing structural coupling metrics and proved to be better predictors for classes impacted by changes.

Mining design patterns from C++ source code

Design patterns are micro architectures that have proved to be reliable, easy-to implement and robust. There is a need in science and industry for recognizing these patterns. We present a new method for discovering design patterns in the source code. This method provides a precise specification of how the patterns work by describing basic structural information like inheritance, composition, aggregation and association, and as an indispensable part, by defining call delegation, object creation and operation overriding. We introduce a new XML-based language, the Design Pattern Markup Language (DPML), which provides an easy way for the users to modify pattern descriptions to suit their needs, or even to define their own patterns or just classes in certain relations they wish to find. We tested our method on four open-source systems, and found it effective in discovering design pattern instances.

Survey of code-size reduction methods

Program code compression is an emerging research activity that is having an impact in several production areas such as networking and embedded systems. This is because the reduced-sized code can have a positive impact on network traffic and embedded system costs such as memory requirements and power consumption. Although code-size reduction is a relatively new research area, numerous publications already exist on it. The methods published usually have different motivations and a variety of application contexts. They may use different principles and their publications often use diverse notations. To our knowledge, there are no publications that present a good overview of this broad range of methods and give a useful assessment. This article surveys twelve methods and several related works appearing in some 50 papers published up to now. We provide extensive assessment criteria for evaluating the methods and offer a basis for comparison. We conclude that it is fairly hard to make any fair comparisons of the methods or draw conclusions about their applicability.

Extracting facts from open source software

Open source software systems are becoming increasingly important these days. 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 without proper management, the quality and reliability of the code may be uncertain. The quality of the code needs to be measured and this can be done only with the help of proper tools. We describe a framework called Columbus with which we calculate the object oriented metrics validated by Basili et al. for illustrating how fault-proneness detection from the open source Web and e-mail suite called Mozilla can be done. We also compare the metrics of several versions of Mozilla to see how the predicted fault-proneness of the software system changed during its development. The Columbus framework has been further developed recently with a compiler wrapping technology that now gives us the possibility of automatically analyzing and extracting information from software systems without modifying any of the source code or makefiles. We also introduce our fact extraction process here to show what logic drives the various tools of the Columbus framework and what steps need to be taken to obtain the desired facts.

Modeling class cohesion as mixtures of latent topics

The paper proposes a new measure for the cohesion of classes in Object-Oriented software systems. It is based on the analysis of latent topics embedded in comments and identifiers in source code. The measure, named as Maximal Weighted Entropy, utilizes the Latent Dirichlet Allocation technique and information entropy measures to quantitatively evaluate the cohesion of classes in software. This paper presents the principles and the technology that stand behind the proposed measure. Two case studies on a large open source software system are presented. They compare the new measure with an extensive set of existing metrics and use them to construct models that predict software faults. The case studies indicate that the novel measure captures different aspects of class cohesion compared to the existing cohesion measures and improves fault prediction for most metrics, which are combined with Maximal Weighted Entropy.

New Conceptual Coupling and Cohesion Metrics for Object-Oriented Systems

The paper presents two novel conceptual metrics for measuring coupling and cohesion in software systems. Our first metric, Conceptual Coupling between Object classes (CCBO), is based on the well-known CBO coupling metric, while the other metric, Conceptual Lack of Cohesion on Methods (CLCOM5), is based on the LCOM5 cohesion metric. One advantage of the proposed conceptual metrics is that they can be computed in a simpler (and in many cases, programming language independent) way as compared to some of the structural metrics. We empirically studied CCBO and CLCOM5 for predicting fault-proneness of classes in a large open source system and compared these metrics with a host of existing structural and conceptual metrics for the same task. As the result, we found that the proposed conceptual metrics, when used in conjunction, can predict bugs nearly as precisely as the 58 structural metrics available in the Columbus source code quality framework and can be effectively combined with these metrics to improve bug prediction.

Towards a standard schema for C/C++

Developing a standard schema at the abstract syntax tree (AST) level for C/C++ to be used by reverse engineering and reengineering tools is a complex and difficult problem. In this paper we present a catalogue of issues that need to be considered in order to design a solution. Three categories of issues are discussed. Lexical structure is the first category and pertains to characteristics of the source code, such as spaces and comments. The second category, syntax, includes both the mundane and hard problems in the C++ programming language. The final category is semantics and covers aspects such as naming and reference resolution. Example solutions to these challenges are provided from the Datrix schema from Bell Canada and the Columbus schema from University of Szeged. The paper concludes with a discussion of lessons learnt and plans for future work on a C/C++AST standard schema.