An Empirical investigation into metrics for object-oriented software

Object-Oriented methods have increased in popularity over the last decade, and are now the norm for software development in many application areas. Many claims were made for the superiority of object-oriented methods over more traditional methods, and these claims have largely been accepted, or at least not questioned by the software community. Such was the motivation for this thesis. One way of capturing information about software is the use of software metrics. However, if we are to have faith in the information, we must be satisfied that these metrics do indeed tell us what we need to know. This is not easy when the software characteristics we are interested in are intangible and unable to be precisely defined. This thesis considers the attempts to measure software and to make predictions regarding maintainabilty and effort over the last three decades. It examines traditional software metrics and considers their failings in the light of the calls for better standards of validation in terms of measurement theory and empirical study. From this five lessons were derived. The relatively new area of metrics for object-oriented systems is examined to determine whether suggestions for improvement have been widely heeded. The thesis uses an industrial case study and an experiment to examine one feature of objectorientation, inheritance, and its effect on aspects of maintainability, namely number of defects and time to implement a change. The case study is also used to demonstrate that it is possible to obtain early, simple and useful local prediction systems for important attributes such as system size and defects, using readily available measures rather than attempting predefined and possibly time consuming metrics which may suffer from poor definition, invalidity or inability to predict or capture anything of real use. The thesis concludes that there is empirical evidence to suggest a hypothesis linking inheritance and increased incidence of defects and increased maintenance effort and that more empirical studies are needed in order to test the hypothesis. This suggests that we should treat claims regarding the benefits of object-orientation for maintenance with some caution. This thesis also concludes that with the ability to produce, with little effort, accurate local metrics, we have an acceptable substitute for the large predefined metrics suites with their attendant problems

Next generation software environments : principles, problems, and research directions

The past decade has seen a burgeoning of research and development in software environments. Conferences have been devoted to the topic of practical environments, journal papers produced, and commercial systems sold. Given all the activity, one might expect a great deal of consensus on issues, approaches, and techniques. This is not the case, however. Indeed, the term "environment" is still used in a variety of conflicting ways. Nevertheless substantial progress has been made and we are at least nearing consensus on many critical issues.The purpose of this paper is to characterize environments, describe several important principles that have emerged in the last decade or so, note current open problems, and describe some approaches to these problems, with particular emphasis on the activities of one large-scale research program, the Arcadia project. Consideration is also given to two related topics: empirical evaluation and technology transition. That is, how can environments and their constituents be evaluated, and how can new developments be moved effectively into the production sector

Object-Oriented Software Design Metrics

The adoption of the Object-Oriented paradigm is expected to help produce better and cheaper software. The main concepts of this paradigm, namely, inheritance, encapsulation, information hiding or polymorphism, are the keys to foster reuse and achieve easier maintainability. However, the use of constructs that support those concepts can be more or less intensive, mainly depending on the designer ability. Advances in quality and productivity need to be correlated with the use of those constructs. Therefore, we need to evaluate them quantitatively to guide OO design. The availability of these metrics should allow comparison of different systems or different implementations of the same system, thus helping to derive some design heuristics that could/should be included in design tools. Those heuristics would at least be a valuable help to new staff members. "Blind" choice (or creation) is dangerous, so a set of common requirements for metrics and corresponding rationale was introduced, which includes the need for formal definition, language independence, dimensionlessness, ease of calculation and early obtainability. A suitable metrics set named MOOD was then proposed. We believe that these metrics can help in setting OO design standards at the organization level, helping OO practitioners to guide their development process and, hopefully, leaving them in a cheerful MOOD..

Using a Combination of Measurement Tools to Extract Metrics from Open Source Projects

Software measurement can play a major role in ensuring the quality and reliability of software products. The measurement activities require appropriate tools to collect relevant metric data. Currently, there are several such tools available for software measurement. The main objective of this paper is to provide some guidelines in using a combination of multiple measurement tools especially for products built using object-oriented techniques and languages. In this paper, we highlight three tools for collecting metric data, in our case from several Java-based open source projects. Our research is currently based on the work of Card and Glass, who argue that design complexity measures (data complexity and structural complexity) are indicators/predictors of procedural/cyclomatic complexity (decision counts) and errors (discovered from system tests). Their work was centered on structured design and our work is with object-oriented designs and the metrics we use parallel those of Card and Glass, being, Henry and Kafura's Information Flow Metrics, McCabe's Cyclomatic Complexity, and Chidamber and Kemerer Object-oriented Metrics

Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation

We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.Comment: To appear in 2nd Conference on Robot Learning (CoRL) 201