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

Software Metrics Evaluation Based on Entropy

Software engineering activities in the Industry has come a long way with various improve- ments brought in various stages of the software development life cycle. The complexity of modern software, the commercial constraints and the expectation for high quality products demand the accurate fault prediction based on OO design metrics in the class level in the early stages of software development. The object oriented class metrics are used as quality predictors in the entire OO software development life cycle even when a highly iterative, incremental model or agile software process is employed. Recent research has shown some of the OO design metrics are useful for predicting fault-proneness of classes. In this paper the empirical validation of a set of metrics proposed by Chidamber and Kemerer is performed to assess their ability in predicting the software quality in terms of fault proneness and degradation. We have also proposed the design complexity of object-oriented software with Weighted Methods per Class metric (WMC-CK metric) expressed in terms of Shannon entropy, and error proneness

Patterns of Change: Can modifiable software have high coupling?

There are few aspects of modern life that remain unaffected by software, and as our day-to-day challenges change, so too must our software. Software systems are complex, and as they grow larger and more interconnected, they become more difficult to modify due to excessive change propagation. This is known as the ripple effect. The primary strategies to mitigate it are modular design, and minimization of coupling, or between-module interaction. However, analysis of complex networks has shown that many are scale-free, which means that they contain some components that are highly connected. The presence of scale-free structure implies high coupling, which suggests that software systems may be hard to modify because they suffer from the ripple effect. In this thesis, a large corpus of open-source software systems is analysed to determine whether software systems are scale-free, whether scale-free structure results in high coupling, and whether high coupling results in ripple effects that propagate change to a large proportion of classes. The results show that all systems in the corpus are scale-free and that that property results in high coupling. However, analysis of system evolution reveals that existing code is modified infrequently and that there is rarely sufficient evidence to be confident that ripple effects involving a high proportion of classes have actually occurred. This thesis concludes first that while it is desirable to avoid excessive interconnectivity, it is difficult to completely eliminate high coupling; and second, that the presence of high coupling does not necessarily imply poor system design

Software quality attribute measurement and analysis based on class diagram metrics

Software quality measurement lies at the heart of the quality engineering process. Quality measurement for object-oriented artifacts has become the key for ensuring high quality software. Both researchers and practitioners are interested in measuring software product quality for improvement. It has recently become more important to consider the quality of products at the early phases, especially at the design level to ensure that the coding and testing would be conducted more quickly and accurately. The research work on measuring quality at the design level progressed in a number of steps. The first step was to discover the correct set of metrics to measure design elements at the design level. Chidamber and Kemerer (C&K) formulated the first suite of OO metrics. Other researchers extended on this suite and provided additional metrics. The next step was to collect these metrics by using software tools. A number of tools were developed to measure the different suites of metrics; some represent their measurements in the form of ordinary numbers, others represent them in 3D visual form. In recent years, researchers developed software quality models which went a bit further by computing quality attributes from collected design metrics. In this research we extended on the software quality modelers’ work by adding a quality attribute prioritization scheme and a design metric analysis layer. Our work is all focused on the class diagram, the most fundamental constituent in any object oriented design. Using earlier researchers’ work, we extract a class diagram’s metrics and compute its quality attributes. We then analyze the results and inform the user. We present our figures and observations in the form of an analysis report. Our target user could be a project manager or a software quality engineer or a developer who needs to improve the class diagram’s quality. We closely examine the design metrics that affect quality attributes. We pinpoint the weaknesses in the class diagram, based on these metrics, inform the user about the problems that emerged from these classes, and advice him/her as to how he/she can go about improving the overall design quality. We consider the six basic quality attributes: “Reusability”, “Functionality”, “Understandability”, “Flexibility”, “Extendibility”, and “Effectiveness” of the whole class diagram. We allow the user to set priorities on these quality attributes in a sequential manner based on his/her requirements. Using a geometric series, we calculate a weighted average value for the arranged list of quality attributes. This weighted average value indicates the overall quality of the product, the class diagram. Our experimental work gave us much insight into the meanings and dependencies between design metrics and quality attributes. This helped us refine our analysis technique and give more concrete observations to the user

The Automated analysis of object-oriented designs

This thesis concerns the use of software measures to assess the quality of object-oriented designs. It examines the ways in which design assessment can be assisted by measurement and the areas in which it can't. Other work in software measurement looks at defining and validating measures,or building prediction systems. This work is distinctive in that it examines the use of measures to help improve design quality during design time. To evaluate a design based on measurement results requires a means of relating measurement values to particular design problems or quality levels. Design heuristics were used to make this connection between measurement and quality. A survey was carried out to find suggestions for guidelines, rules and heuristics from the 00 design literature. This survey resulted in a catalogue of 288 suggestions for 00 design heuristics. The catalogue was structured around the 00 constructs to which the heuristics relate, and includes information on various heuristic attributes. This scheme is intended to allow suitable heuristics to be quickly located and correctly applied. Automation requires tool support. A tool was built which augmented the functionality available in existing sets, and taking input from multiple sources of design information (e.g., CASE tools and source code) and the described so far presents a potential method for automated design assessment provides the means of automation. An empirical study was then required to consider the efficacy of the method and evaluate the novel features of the tool. A case study was used to explore the approach taken by, and evaluate the effectiveness of, 15 subjects using measures and heuristics to assess the design of a small 00 system(IS classes). This study showed that semantic heuristics tended to highlight significant problems, but where attempts were made to automate these it often led to false problems being identified. This result, along with a previous finding that around half of quality criteria are not automatically assessable at design time, strongly suggeststhat people are still a necessary part of design assessment. The main result of the case study was that the subjects correctly identified 90% of the major design problems and were very positive about their experience of using measurement to support design assessment