Dealing with inheritance in OO evolutionary testing.

Most of the software developed in the world follows the object-oriented (OO) paradigm. However, the existing work on evolutionary testing is mainly targeted to procedural languages. All this work can be used with small changes on OO programs, but object orientation introduces new features that are not present in procedural languages. Some important issues are polymorphism and inheritance. In this paper we want to make a contribution to the inheritance field by proposing some approaches that use the information of the class hierarchy for helping test case generators to better guide the search. To the best of our knowledge, no work exists using this information to propose test cases. In this work we de¯ne a branch distance for logical expressions containing the instanceof operator in Java programs. In addition to the distance measure, we propose two mutation operators based on the distance. We study the behaviour of the mutation operators on a benchmark set composed of nine OO programs. The results show that the information collected from the class hierarchy helps in the search for test cases

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions. When applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for quality. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique. In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of Software Engineering (ICSE), 201

Management implications of moving from a traditional structured systems development methodology to object-orientation

Thesis (M.S.) University of Alaska Fairbanks, 2003As software application systems become larger and more complex, many software employers and managers believe that the key to sustaining its competitive advantage in the computing technology market lies in its software engineering capabilities. Software crisis situation seems to be a common occurrence in the software development environment as systems become larger and more complex. Object Orientation (OO) has been proposed as a viable alternative to traditional approach (i.e., structured techniques), an approach that many hope will solve the current software crisis. 00 is a new paradigm, and it requires new types of knowledge, new specialists, and significant changes in the mindset, an entirely different way of thinking, representing and solving a problem. The transition of moving toward the 00 from the traditional approach may involve a high risk of failure if the managers do not understand the nature of paradigm shifts and do not anticipate the future. The problem of moving to 00 has become very important. An understanding of potential problems from migrating to the new paradigm helps managers make a smoother paradigm shift. The implications and challenges of the 00 paradigm are presented. The study suggests that Object-Oriented System Development (OOSD) requires more discipline, management and training than traditional software development does. Education and experience are keys for the success of any OOSD project

A survey on software testability

Context: Software testability is the degree to which a software system or a unit under test supports its own testing. To predict and improve software testability, a large number of techniques and metrics have been proposed by both practitioners and researchers in the last several decades. Reviewing and getting an overview of the entire state-of-the-art and state-of-the-practice in this area is often challenging for a practitioner or a new researcher. Objective: Our objective is to summarize the body of knowledge in this area and to benefit the readers (both practitioners and researchers) in preparing, measuring and improving software testability. Method: To address the above need, the authors conducted a survey in the form of a systematic literature mapping (classification) to find out what we as a community know about this topic. After compiling an initial pool of 303 papers, and applying a set of inclusion/exclusion criteria, our final pool included 208 papers. Results: The area of software testability has been comprehensively studied by researchers and practitioners. Approaches for measurement of testability and improvement of testability are the most-frequently addressed in the papers. The two most often mentioned factors affecting testability are observability and controllability. Common ways to improve testability are testability transformation, improving observability, adding assertions, and improving controllability. Conclusion: This paper serves for both researchers and practitioners as an "index" to the vast body of knowledge in the area of testability. The results could help practitioners measure and improve software testability in their projects

On the Value of Quality Attributes for Refactoring Model Transformations Using a Multi-Objective Algorithm

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152454/1/QMOOD_for_ATL__Copy_.pd

An empirical investigation of inheritance trends in JAVA OSS evolution

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Inheritance is a salient feature of Object-Oriented (OO) paradigm which facilitates reuse and improves system comprehensibility in OO systems. The overall aim of inheritance is to model classes in a structured hierarchy where classes residing lower in the hierarchy (subclasses) can inherit the pre-existing functionality in the classes located higher up (superclasses) in the same line of hierarchy. Software maintenance and evolution are the process of making any modifications to a software system and upgrading its dynamic behaviour. In this Thesis, we empirically investigate the trends of evolution of eight Java Open-Source Systems (OSS) from an inheritance perspective and model the propensity for changes of inheritance in those systems. The systems used as testbed in this Thesis represent a variety of application domains with varying sizes and amount of inheritance employed. There are several levels of granularity for inheritance evolution that may manifest a particular trend. This starts from the highest level (package) to lower class, method an attribute levels; and each level may show a different and yet an important pattern of evolution. We empirically investigate the changes of inheritance in the form of increases (additions) and decreases (deletions) in number of classes, methods and attributes. Our analysis also includes the movement of classes within and across an inheritance hierarchy which is another compelling facet of evolution of inheritance and may not be extrapolated through incremental changes only. It requires a finer-grained scrutiny of evolutionary traits of inheritance. In addition, the Thesis also explores the trends of class interaction within and across an inheritance hierarchy and problems embedded in a system that may lead to faults, from an inheritance perspective. The results demonstrate how inheritance is used in practice, problems associated with inheritance and how inheritance hierarchies evolve as opposed to that of a ‘system’. Overall results informed our understanding of the trends in changes of inheritance in the evolution of Java systems

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems