15,870 research outputs found
An Approach for the Empirical Validation of Software Complexity Measures
Software metrics are widely accepted tools to control and assure software quality. A large number of software metrics with a variety of content can be found in the literature; however most of them are not adopted in industry as they are seen as irrelevant to needs, as they are unsupported, and the major reason behind this is due to improper
empirical validation. This paper tries to identify possible root causes for the improper empirical validation of the software metrics. A practical model for the empirical validation of software metrics is proposed along with root causes. The model is validated by applying it to recently proposed and well known metrics
A survey on software coupling relations and tools
Context
Coupling relations reflect the dependencies between software entities and can be used to assess the quality of a program. For this reason, a vast amount of them has been developed, together with tools to compute their related metrics. However, this makes the coupling measures suitable for a given application challenging to find.
Goals
The first objective of this work is to provide a classification of the different kinds of coupling relations, together with the metrics to measure them. The second consists in presenting an overview of the tools proposed until now by the software engineering academic community to extract these metrics.
Method
This work constitutes a systematic literature review in software engineering. To retrieve the referenced publications, publicly available scientific research databases were used. These sources were queried using keywords inherent to software coupling. We included publications from the period 2002 to 2017 and highly cited earlier publications. A snowballing technique was used to retrieve further related material.
Results
Four groups of coupling relations were found: structural, dynamic, semantic and logical. A fifth set of coupling relations includes approaches too recent to be considered an independent group and measures developed for specific environments. The investigation also retrieved tools that extract the metrics belonging to each coupling group.
Conclusion
This study shows the directions followed by the research on software coupling: e.g., developing metrics for specific environments. Concerning the metric tools, three trends have emerged in recent years: use of visualization techniques, extensibility and scalability. Finally, some coupling metrics applications were presented (e.g., code smell detection), indicating possible future research directions. Public preprint [https://doi.org/10.5281/zenodo.2002001]
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
Improving Reuse of Distributed Transaction Software with Transaction-Aware Aspects
Implementing crosscutting concerns for transactions is difficult, even using Aspect-Oriented Programming Languages (AOPLs) such as AspectJ. Many of these challenges arise because the context of a transaction-related crosscutting concern consists of loosely-coupled abstractions like dynamically-generated identifiers, timestamps, and tentative value sets of distributed resources. Current AOPLs do not provide joinpoints and pointcuts for weaving advice into high-level abstractions or contexts, like transaction contexts. Other challenges stem from the essential complexity in the nature of the data, operations on the data, or the volume of data, and accidental complexity comes from the way that the problem is being solved, even using common transaction frameworks. This dissertation describes an extension to AspectJ, called TransJ, with which developers can implement transaction-related crosscutting concerns in cohesive and loosely-coupled aspects. It also presents a preliminary experiment that provides evidence of improvement in reusability without sacrificing the performance of applications requiring essential transactions. This empirical study is conducted using the extended-quality model for transactional application to define measurements on the transaction software systems. This quality model defines three goals: the first relates to code quality (in terms of its reusability); the second to software performance; and the third concerns software development efficiency. Results from this study show that TransJ can improve the reusability while maintaining performance of TransJ applications requiring transaction for all eight areas addressed by the hypotheses: better encapsulation and separation of concern; loose Coupling, higher-cohesion and less tangling; improving obliviousness; preserving the software efficiency; improving extensibility; and hasten the development process
A COUPLING AND COHESION METRICS SUITE FOR
The increasing need for software quality measurements has led to extensive research
into software metrics and the development of software metric tools. To maintain high
quality software, developers need to strive for a low-coupled and highly cohesive
design. One of many properties considered when measuring coupling and cohesion is the
type of relationships that made up coupling and cohesion. What these specific
relationships are is widely understood and accepted by researchers and practitioners.
However, different researchers base their metrics on a different subset of these
relationships.
Studies have shown that because of the inclusion of multiple subsets of relationships
in one measure of coupling and cohesion metrics, the measures tend to correlate among
each other. Validation of these metrics against maintainability index of a Java program
suggested that there is high multicollinearity among coupling and cohesion metrics.
This research introduces an approach of implementing coupling and cohesion
metrics. Every possible relationship is considered and, for each, addressed the issue of
whether or not it has significant effect on maintainability index prediction. Validation of
orthogonality of the selected metrics is assessed by means of principal component
analysis. The investigation suggested that some of the metrics are independent set of
metrics, while some are measuring similar dimension
Identification, Analysis & Empirical Validation (IAV) of Object Oriented Design (OO) Metrics as Quality Indicators
Metrics and Measure are closely inter-related to each other. Measure is defined as way of defining amount, dimension, capacity or size of some attribute of a product in quantitative manner while Metric is unit used for measuring attribute. Software quality is one of the major concerns that need to be addressed and measured. Object oriented (OO) systems require effective metrics to assess quality of software. The paper is designed to identify attributes and measures that can help in determining and affecting quality attributes. The paper conducts empirical study by taking public dataset KC1 from NASA project database. It is validated by applying statistical techniques like correlation analysis and regression analysis. After analysis of data, it is found that metrics SLOC, RFC, WMC and CBO are significant and treated as quality indicators while metrics DIT and NOC are not significant. The results produced from them throws significant impact on improving software quality
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