Validation of object-oriented software GA metric selection model using domain experts

This study presents validation of object-oriented model to predict its maintainability. The study used metric threshold in its encoding strategy in the implementation of GA Model before being compared with classical model. This empirical validation was then compared with real maintainability data from experts using similar procedures. To understand the overall effect of particular software, linear discriminant analysis which is machine learning statistical method was utilised to evaluate the performance of the metrics. The results pointed out that there is significant relationship when expert’s opinions were used. Experts also indicated the role of inheritance metrics in predicting maintainability of object-oriented software which also highlighted the needs for further empirical investigation on the production of more metrics threshold that give researchers and practitioners an opportunity to work on more metrics

Weighted Class Complexity: A Measure of Complexity for Object Oriented System

Software complexity metrics are used to predict critical information about reliability and maintainability of software systems. Object oriented software development requires a different approach to software complexity metrics. In this paper, we propose a metric to compute the structural and cognitive complexity of class by associating a weight to the class, called as Weighted Class Complexity (WCC). On the contrary, of the other metrics used for object oriented systems, proposed metric calculates the complexity of a class due to methods and attributes in terms of cognitive weight. The proposed metric has been demonstrated with OO examples. The theoretical and practical evaluations based on the information theory have shown that the proposed metric is on ratio scale and satisfies most of the parameters required by the measurement theor

Measuring and assessing maintainability at the end of high level design

Software architecture appears to be one of the main factors affecting software maintainability. Therefore, in order to be able to predict and assess maintainability early in the development process we need to be able to measure the high-level design characteristics that affect the change process. To this end, we propose a measurement approach, which is based on precise assumptions derived from the change process, which is based on Object-Oriented Design principles and is partially language independent. We define metrics for cohesion, coupling, and visibility in order to capture the difficulty of isolating, understanding, designing and validating changes

Exploring Maintainability Assurance Research for Service- and Microservice-Based Systems: Directions and Differences

To ensure sustainable software maintenance and evolution, a diverse set of activities and concepts like metrics, change impact analysis, or antipattern detection can be used. Special maintainability assurance techniques have been proposed for service- and microservice-based systems, but it is difficult to get a comprehensive overview of this publication landscape. We therefore conducted a systematic literature review (SLR) to collect and categorize maintainability assurance approaches for service-oriented architecture (SOA) and microservices. Our search strategy led to the selection of 223 primary studies from 2007 to 2018 which we categorized with a threefold taxonomy: a) architectural (SOA, microservices, both), b) methodical (method or contribution of the study), and c) thematic (maintainability assurance subfield). We discuss the distribution among these categories and present different research directions as well as exemplary studies per thematic category. The primary finding of our SLR is that, while very few approaches have been suggested for microservices so far (24 of 223, ?11%), we identified several thematic categories where existing SOA techniques could be adapted for the maintainability assurance of microservices

Estimation of Defect proneness Using Design complexity Measurements in Object- Oriented Software

Software engineering is continuously facing the challenges of growing complexity of software packages and increased level of data on defects and drawbacks from software production process. This makes a clarion call for inventions and methods which can enable a more reusable, reliable, easily maintainable and high quality software systems with deeper control on software generation process. Quality and productivity are indeed the two most important parameters for controlling any industrial process. Implementation of a successful control system requires some means of measurement. Software metrics play an important role in the management aspects of the software development process such as better planning, assessment of improvements, resource allocation and reduction of unpredictability. The process involving early detection of potential problems, productivity evaluation and evaluating external quality factors such as reusability, maintainability, defect proneness and complexity are of utmost importance. Here we discuss the application of CK metrics and estimation model to predict the external quality parameters for optimizing the design process and production process for desired levels of quality. Estimation of defect-proneness in object-oriented system at design level is developed using a novel methodology where models of relationship between CK metrics and defect-proneness index is achieved. A multifunctional estimation approach captures the correlation between CK metrics and defect proneness level of software modules.Comment: 5 pages, 1 figur

Software design metrics for predicting maintainability of service-oriented software

As the pace of business change increases, service-oriented (SO) solutions should facilitate easier maintainability as underlying business logic and rules change. To date, little effort has been dedicated to considering how the structural properties of coupling and cohesion may impact on the maintainability of SO software products. Moreover, due to the unique design characteristics of Service-Oriented Computing (SOC), existing Procedural and Object-Oriented (OO) software metrics are not sufficient for the accurate measurement of service-oriented design structures. This thesis makes a contribution to the field of SOC, and Software Engineering in general, by proposing and evaluating a suite of design-level coupling and cohesion metrics for predicting the maintainability of service-oriented software products early in the Software Development LifeCycle (SDLC). The proposed metrics can provide the following benefits: i) facilitate design decisions that could lead to the specification of quality SO designs that can be maintained more easily; ii) identify design problems that can potentially have a negative effect on the maintainability of existing service-oriented design structures; and iii) support more effective control of maintainability in the earlier stages of SDLC. More specifically, the following research was conducted as part of this thesis: - A formal mathematical model covering the structural and behavioural properties of service-oriented system design was specified. - Software metrics were defined in a precise, unambiguous, and formal manner using the above model. - The metrics were theoretically validated and empirically evaluated in order to determine the success of this thesis as follows: a. Theoretical validation was based on the property-based software engineering measurement framework. All the proposed metrics were deemed as theoretically valid. b. Empirical evaluation employed a controlled experimental study involving ten participants who performed a range of maintenance tasks on two SO systems developed (and measured using the proposed metrics) specifically for this study. The majority of the experimental outcomes compared favourably with our expectations and hypotheses. More specifically, the results indicated that most of the proposed metrics can be used to predict the maintainability of service-oriented software products early in the SDLC, thereby providing evidence for the validity and potential usefulness of the derived metrics. Nevertheless, a broader range of industrial scale experiments and analyses are required to fully demonstrate the practical applicability of the metrics. This has been left to future work