Software Quality Modeling with Limited Apriori Defect Data (original) (raw)
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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING and SYSTEM SOFTWARE, 2020
Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspe becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software prod Developers have attempted to improve software quality by mining and analyzing software data. In any phase of software development life cycle (SDLC), while huge amount of data is produced, some design, security, or software problems may analyzing software data helps to handle these problems and lead to more accurate and timely delivery of software projects. Various data mining and machine learning studies have been conducted to deal with software engineering tasks such as defect prediction, effort estimation, etc. This study shows the open issues and presents related solutions and recommendations in software engineering, applying data mining and machine learning techniques. Software quality is a field of study and practice that describes the desirable attributes of software products. The performance must be perfect without any defects. Software quality metrics are a subset of software metrics that f software defect prediction model helps in early detection of defects and contributes to their efficient removal and producing a quality software system based on several metrics. The main paper is to help developers identify defects based on existing software metrics using data mining techniques and thereby improve the software quality. In this paper, various classification are revisited which are employed for software defect prediction using software metrics in the literature.
Software quality is a field of study and practice that describes the desirable attributes of software products. The performance must be perfect without any defects.Software quality metrics are a subset of software metrics that focus on the quality aspects of the product, process, and project.The software defectprediction model helps in early detection of defects and contributes to their efficient removal and producing a quality software system based on several metrics. The main objective of paper is to help developers identify defects based on existing software metrics using data mining techniques and thereby improve the software quality.In this paper, variousclassification techniquesare revisitedwhich are employed for software defect prediction using software metrics in the literature.
Data Mining Techniques for Software Quality Prediction
Concepts, Methodologies, Tools, and Applications, 2014
In the present time, software plays a vital role in business, governance, and society in general, so a continuous improvement of software productivity and quality such as reliability, robustness, etc. is an important goal of software engineering. During software development, a large amount of data is produced, such as software attribute repositories and program execution trace, which may help in future development and project management activities. Effective software development needs quantification, measurement, and modelling of previous software artefacts. The development of large and complex software systems is a formidable challenge which requires some additional activities to support software development and project management processes. In this scenario, data mining can provide a helpful hand in the software development process. This chapter discusses the application of data mining in software engineering and includes static and dynamic defect detection, clone detection, maintenance, etc. It provides a way to understand the software artifacts and processes to assist in software engineering tasks.
Software Defect Prediction to Improve Software Quality Using Machine Learning Approach
2021
Software Engineering is most commonly used to build application-based programming, preparation, and deployment. For any technology organization, it is essential that even the most inexperienced software users must produce high-quality software applications. Computer failure prediction is a key aspect to improve the efficiency of software applications. Computer reliability is a model that can accurately predict software failures. Machine Learning (ML) solves various problems by developing programs that can enhance the output of specific processes based on existing knowledge. These algorithms are very useful for developing various forms of applications. This Machine Learning Algorithm focuses on a variety of well-known problem areas in which humans lack the necessary skills to create successful algorithms. Due to the conversion of certain software tasks into learning problems and learning algorithms, the production of programs becomes more tedious.the Rayleigh Model is combined with T...
Fault Prediction Using Statistical and Machine Learning Methods for Improving Software Quality
—An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods
A Literature Review Study of Software Defect Prediction using Machine Learning Techniques
International Journal of Emerging Research in Management &Technology , 2017
oftware systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.
Applying Machine Learning Analysis for Software Quality Test
2023 International Conference on Code Quality (ICCQ)
One of the biggest expense in software development is the maintenance. Therefore, it's critical to comprehend what triggers maintenance and if it may be predicted. Numerous research outputs have demonstrated that specific methods of assessing the complexity of created programs may produce useful prediction models to ascertain the possibility of maintenance due to software failures. As a routine it is performed prior to the release, and setting up the models frequently calls for certain, object-oriented software measurements. It's not always the case that software developers have access to these measurements. In this paper, machine learning is applied on the available data to calculate the cumulative software failure levels. A technique to forecast a software's residual defectiveness using machine learning can be looked into as a solution to the challenge of predicting residual flaws. Software metrics and defect data were separated out of the static source code repository. Static code is used to create software metrics, and reported bugs in the repository are used to gather defect information. By using a correlation method, metrics that had no connection to the defect data were removed. This makes it possible to analyze all the data without pausing the programming process. Large, sophisticated software's primary issue is that it is impossible to control everything manually, and the cost of an error can be quite expensive. Developers may miss errors during testing as a consequence, which will raise maintenance costs. Finding a method to accurately forecast software defects is the overall objective.
Fault prediction modeling for software quality estimation: Comparing commonly used techniques
Empirical Software Engineering, 2003
High-assurance and complex mission-critical software systems are heavily dependent on reliability of their underlying software applications. An early software fault prediction is a proven technique in achieving high software reliability. Prediction models based on software metrics can predict number of faults in software modules. Timely predictions of such models can be used to direct costeffective quality enhancement efforts to modules that are likely to have a high number of faults. We evaluate the predictive performance of six commonly used fault prediction techniques: CART-LS (least squares), CART-LAD (least absolute deviation), S-PLUS, multiple linear regression, artificial neural networks, and case-based reasoning. The case study consists of software metrics collected over four releases of a very large telecommunications system. Performance metrics, average absolute and average relative errors, are utilized to gauge the accuracy of different prediction models. Models were built using both, original software metrics (RAW) and their principle components (PCA). Two-way ANOVA randomized-complete block design models with two blocking variables are designed with average absolute and average relative errors as response variables. System release and the model type (RAW or PCA) form the blocking variables and the prediction technique is treated as a factor. Using multiple-pairwise comparisons, the performance order of prediction models is determined. We observe that for both average absolute and average relative errors, the CART-LAD model performs the best while the S-PLUS model is ranked sixth.
Improved software defect prediction
2005
Although a number of approaches have been taken to quality prediction for software, none have achieved widespread applicability. This paper describes a single model to combine the diverse forms of, often causal, evidence available in software development in a more natural and efficient way than done previously. We use Bayesian Networks as the appropriate formalism for representing defect introduction, detection and removal processes throughout any life-cycle. The approach combines subjective judgements from experienced project managers and available defect rate data to produce a risk map and use this to forecast and control defect rates. Moreover, the risk map more naturally mirrors real world influences without any distracting mathematical formality. The paper focuses on the extensive validation of the approach within Philips Consumer Electronics (dozens of diverse projects across Philips internationally). The resulting model (packaged within a commercial software tool, AgenaRisk, usable by project managers) is now being used to predict defect rates at various testing and operational phases. The results of the validation confirm that the approach is scalable, robust and more accurate that can be achieved using classical methods. We have found 95% correlation between actual and predicted defects. The defect prediction models incorporate cutting-edge ideas and results from software metrics and process improvement research and package them as risk templates that can either be applied either offthe-shelf or after calibrating them to local conditions and to suit the software development processes in use.
A Review on Software Defect Prediction Techniques Using Product Metrics
International Journal of Database Theory and Application, 2017
Presently, complexity and volume of software systems are increasing with a rapid rate. In some cases it improves performance and brings efficient outcome, but unfortunately in several situations it leads to elevated cost for testing, meaningless outcome and inferior quality, even there is no trustworthiness of the products. Fault prediction in software plays a vital role in enhancing the software excellence as well as it helps in software testing to decrease the price and time. Conventionally, to describe the difficulty and calculate the duration of the programming, software metrics can be utilized. To forecast the amount of faults in module and utilizing software metrics, an extensive investigation is performed. With the purpose of recognizing the causes which importantly enhances the fault prediction models related to product metrics, this empirical research is made. This paper visits various software metrics and suggested procedures through which software defect prediction is enhanced and also summarizes those techniques.