Software quality assessment model: a systematic mapping study

Aims: Software quality assurance is a formal process for evaluating and documenting the quality of the work products during each stage of the software development lifecycle.The practice of applying software metrics to operational factors and to maintain factors is a complex task.Successful software quality assurance is highly dependent on software metrics.It needs linkage the software quality model and software metrics through quality factors in order to offer measure method for software quality assurance.The contributions of this paper build an appropriate method of Software quality metrics application in quality life cycle with software quality assurance.Design: The purpose approach defines some software metrics in the factors and discussed several software quality assurance model and some quality factors measure method.Methodology: This paper solves customer value evaluation problem are: Build a framework of combination of software quality criteria.Describes software metrics.Build Software quality metrics application in quality life cycle with software quality assurance.Results: From the appropriate method of Software quality metrics application in quality life cycle with software quality assurance, each activity in the software life cycle, there is one or more QA quality measure metrics focus on ensuring the quality of the process and the resulting product.Future research is need to extend and improve the methodology to extend metrics that have been validated on one project, using our criteria, valid measures of quality on future software project.

Empirical Analysis of Quality Models in Practice in Small IT Companies in SEE Region

Current software quality estimation models often involve the use of supervised learning methods for building a software fault prediction models. In such models, dependent variable usually represents a software quality measurement indicating the quality of a module by risk-basked class membership, or the number of faults. Independent variables include various software metrics as McCabe, Error Count, Halstead, Line of Code, etc... In this paper we present the use of advanced tool for data mining called Multimethod on the case of building software fault prediction model. Multimethod combines different aspects of supervised learning methods in dynamical environment and therefore can improve accuracy of generated prediction model. We demonstrate the use Multimethod tool on the real data from the Metrics Data Project Data (MDP) Repository. Our preliminary empirical results show promising potentials of this approach in predicting software quality in a software measurement and quality dataset.

Summary form only given. The primary objective of introducing metrics and measurement is to improve software quality, and thus the productivity of the organization. Implementation of a software quality measurement effort, however, is not without cost. Therefore, it is important to understand that introducing software metrics and measurement may result in increasing up-front costs. Some consideration should be given to performing a cost benefit analysis to help determine if expectations are realistic and if the predefined goals are feasible. The identification of desired outcomes/results and the selection of appropriate measurement criteria are key to the success of a software improvement program.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Object-oriented software metrics are important in software engineering field for measurement of the software quality, size estimation, complexity and effort. Object oriented software development is the most popular and best practice in software development. Object oriented software metrics can be used for measuring extend of object orientedness of a program. These quantities are important information indicators, that can be helpful for a software developer in take decisions for designing good software. It is proven that the software metrics provide quantitative information about the structure of the code, which can be used to determine the reliability of software. The design of the software is highly recommended to be object oriented in the software development environment. Software metrics specific to programs that have object oriented design can be used to measure the quality of the software like inheritance, assessing the effects of coupling, the complexity of the code, size aspects, etc, of the software developed in such an environment. Software measurement is a way for assessing the quality of software. Many attributes can define quality of a software, internal (size, complexity) as well as external (reliability)[14]. Often internal attributes are contributing factors to external attributes. Reliability as a term means the degree to which a component, product or system executes specified functions for a specific period of time under specified conditions. Software reliability is the software being trustworthy of the specifications. The aim of the work is to model the relationship between reliability of object oriented software to the object oriented design metrics and predict reliability of software.

Software metrics provide a quantitative basis for planning and predicting software development processes. Therefore the quality of software can be controlled and improved easily. Quality in fact aids higher productivity, which has brought software metrics to the forefront. This research paper focuses on different views on software quality. Moreover, many metrics and models have been developed; promoted and utilized resulting in remarkable successes. This paper examines the realm of software engineering to see why software metrics are needed and also reviews their contribution to software quality and reliability. Results can be improved further as we acquire additional experience with variety of software metrics. These experiences can yield tremendous benefits and betterment in quality and reliability.

Several important aspects of software product quality can be evaluated using dynamic metrics that effectively capture and reflect the software's true runtime behavior. While the extent of research in this field is still relatively limited, particularly when compared to research on static metrics, the field is growing, given the inherent advantages of dynamic metrics. The aim of this work is to systematically investigate the body of research on dynamic software metrics to identify issues associated with their selection, design and implementation. Mapping studies are being increasingly used in software engineering to characterize an emerging body of research and to identify gaps in the field under investigation. In this study we identified and evaluated 60 works based on a set of defined selection criteria. These studies were further classified and analyzed to identify their relativity to future dynamic metrics research. The classification was based on three different facets: research focus, research type and contribution type. We found a strong body of research related to dynamic coupling and cohesion metrics, with most works also addressing the abstract notion of software complexity. Specific opportunities for future work relate to a much broader range of quality dimensions.

Success or failure of any software system mainly depends on its quality. Quality plays a key role for determining the acceptability and adoptability of the software system among the users. Quality is the composite characteristic that depends on its different factors. The researchers have been focusing on different quality factors as per their research work requirement and various quality factors have been proposed by them in different quality models to assess the software quality. These models show that there are differences in the perception of the researchers regarding the important quality factors. Ample research work has been done in the field of software quality but till date no work has been done to prove as which quality factors are frequently used and have high importance. This paper is an attempt to find out the major key factors of the software quality. Various quality models have been considered for this purpose and accordingly data set is prepared. Apriori algorithm is used to generate the frequent quality factors. Experiment is performed in Java programming language. The aim of this research paper is to provide proper focus on the frequently used quality factors, which facilitate to improve the quality of software system. These quality factors will be useful to assess the quality of software system in a prudent manner.

Software metrics are used to measure different attributes of software. To practically measure software attributes using these metrics, metric thresholds are needed. Many researchers attempted to identify these thresholds based on personal experiences. However, the resulted experience-based thresholds cannot be generalized due to the variability in personal experiences and the subjectivity of opinions. The goal of this paper is to propose an automated clustering framework based on the expectation maximization (EM) algorithm where clusters are generated using a simplified 3-metric set (LOC, LCOM, and CBO). Given these clusters, different threshold levels for software metrics are systematically determined such that each threshold reflects a specific level of software quality. The proposed framework comprises two major steps: the clustering step where the software quality historical dataset is decomposed into a fixed set of clusters using the EM algorithm, and the threshold extraction step where thresholds, specific to each software metric in the resulting clusters, are estimated using statistical measures such as the mean (μ) and the standard deviation (σ) of each software metric in each cluster. The paper’s findings highlight the capability of EM-based clustering, using a minimum metric set, to group software quality datasets according to different quality levels.

The latest version of programming language Ada, Ada 2012, has introduced the concept of contract-based programming (CBP) and became the first internationally standardized programming language to include CBP as an intrinsic feature of the language. CBP can strictly stipulate and assure the correctness of programs to enhance the reliability and security of safety-critical systems, due to terrible design and/or programming practice, there is an issue that it might obstruct some other factors of the software quality. Therefore, it is essential to implement a supporting environment for CBP with Ada 2012 in order to not only retain reliability by using CBP, but also avoid taking interference to other factors of software quality. Until now, there is no report for proposing supporting environments or tools for CBP, while most studies focus on how to check the conditions of contracts, i.e., what contracts should do for software engineering activities. To support CBP with Ada 2012, this paper analyzes the issues that CBP disturbs some other factors of software quality, proposes methods to avoid the issues, and shows a supporting environment for CBP in Ada 2012 programs.

Despite the importance of software qualities, they are not well understood, especially in the context of the interrelationships between qualities. A number of systematic mapping studies have been conducted prior to 2015 to summarize the literature on the topic and to identify research gaps. To provide a better understanding of the current state of the art, we conducted a systematic mapping study on relevant studies from 2015 to 2019 through a database search and a subsequent snowballing approach. In total, 18 studies were selected as the study subjects wherein we evaluated the types of software quality interrelationships and the qualities that comprise them. Based on our findings, we report on the progress made to address previously identified research gaps.

The selection of software metrics for building software quality prediction models is a search‐based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault‐proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real‐world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85were eliminated. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Importance of construction of models for predicting software quality attributes is increasing leading to usage of artificial intelligence techniques such as Artificial Neural Network (ANN). The goal of this paper is to empirically compare traditional strategies such as Logistic Regression (LR) and ANN to assess software quality. The study used data collected from public domain NASA data set. We find the effect of software metrics on fault proneness. The fault proneness models were predicted using LR regression and ANN methods. The performance of the two methods was compared by Receiver Operating Characteristic (ROC) analysis. The areas under the ROC curves are 0.78 and 0.745 for the LR and ANN model, respectively. The predicted model shows that software metrics are related to fault proneness. The models predict faulty classes with more than 70 percent accuracy. The study showed that ANN method can also be used in constructing software quality models and more similar studies should further investigate the issue. Based on these results, it is reasonable to claim that such a model could help for planning and executing testing by focusing resources on fault-prone parts of the design and code.Keywordsempirical validationmetricssoftware qualityartificial neural network

Putting Requirements and Quality at the Core of Global Service Delivery: Current Efforts and Future Plans at Pace University

Many problems, related with safety, frequently occur because Digital Instrument &amp; Control Systems are widely used and expanding their ranges to many applications in Nuclear Power Plants. It, however, does not hold a general position to estimate an appropriate software quality. Thus, the Quality Characteristic Value, a software quality factor through each software life cycle, is suggested in this paper. The Quality Characteristic Value is obtained as following procedure: 1) Scoring Quality Characteristic Factors (especially correctness, traceability, completeness, and understandability) onto Software Verification and Validation results, 2) Deriving the diamond-shaped graphs by setting values of Factors at each axis and lining every points, and lastly 3) Measuring the area of the graph for Quality Characteristic Value. In this paper, this methodology is applied to Plant Control System. In addition, the series of quantification frameworks exhibit some good characteristics in the view of software quality factor. More than any thing else, it is believed that introduced framework may be applicable to regulatory guide, software approval procedures, due to its soundness and simple characteristics.