Software Defect Prediction Based on Classication Rule Mining

There has been rapid growth of software development. Due to various causes, the software comes with many defects. In Software development process, testing of software is the main phase which reduces the defects of the software. If a developer or a tester can predict the software defects properly then, it reduces the cost, time and eort. In this paper, we show a comparative analysis of software defect prediction based on classifcation rule mining. We propose a scheme for this process and we choose different classication algorithms. Showing the comparison of predictions in software defects analysis. This evaluation analyzes the prediction performance of competing learning schemes for given historical data sets(NASA MDP Data Set). The result of this scheme evaluation shows that we have to choose different classifer rule for different data set

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Software development and correction estimation in the automotive domain

Während der letzten Jahrzehnte hat sich Software in alle Lebensbereiche ausgebreitet. Die kontinuierlich steigenden Kundenanforderungen ließen auch die Komplexität steigen, bei gleichbleibender Produktqualität. Analysedaten und diverse Beispiele entstammen der Automobildomäne, die einen sicherheitskritischen Bereich darstellt, in dem Produkte mit speziellen Qualitätsanforderungen entwickelt werden. Qualitätsanforderungen müssen von diversen Prozessen und Standards bedient werden, bei gleichzeitiger Einhaltung enger Endtermine. Die Komplexität der Software und der Safety-Aspekt beeinflussen die Fehlerquote der Produkte stark. Viele Anforderungen werden während der Entwicklung hinzugefügt oder verändert und führen zu permanenten Änderungen in der Software und einer weiteren Steigerung der Komplexität. Änderungen müssen analysiert und getestet werden, um die Qualität des entstehenden Produktes zu gewährleisten. Die Vorhersage von Defekten und Änderungen in der Software sind ein wichtiger Anteil des Software Engineering. Die industrielle Software-Entwicklung muss ihr Ziel innerhalb diverser Grenzen erreichen, ganz wichtig ist das Budget, wobei sich Änderungen an Projektparametern negativ auf das geplante Budget auswirken können. Solche Änderungen werden in zwei Klassen eingeteilt, durch Kunden verursachte neue oder veränderte Anforderungen, und die Korrekturen, die durch Systemverbesserungen oder Fehlerbehebungen entstehen, beide Klassen für das Projekt-Budget relevant. Die Aufwände für die neuen Kundenanforderungen können dem Budget einfach aufgeschlagen werden. Die Korrekturen verursachen ebenfalls große Aufwände, die zu einem negativen Budget führen können, was eine große Herausforderung für das Projektmanagement wie auch die automatisierte Schätzung der Aufwände über die gesamte Projektlaufzeit darstellt.Over the past decades, software has spread to most areas of our lives. The complexity increased due to steadily increasing customer demands and, at the same time, the high quality of the products had to be kept. The data for the analyses and many of the examples are taken out of the automotive software development domain. The automotive domain is a safety-critical area where products are developed with specific quality requirements. These quality requirements have to be met by many processes and by satisfying several standards within stipulated deadlines during the development lifecycle. The complexity of the software and the safety aspect have a strong influence on the product defect ratio. Many requirements will be added and adjusted during the development lifecycle leading to continuous changes in the software and increased complexity. All these changes need to be analyzed and tested to ensure the quality of the product. Predicting software defects and changes is a significant part of software engineering. Industrial software development has to achieve its target within several boundaries. One of the important boundaries for an industrial project is the budget, where changes of any project parameters can easily lead to negative effects in the planned budget. Such changes are classified into two types, the changes pushed by the customer as new requirements or changed requirements, and the correction changes in the project because of improvements of the system and identified bugs with their fixes. This classification is important to control the project budget. The effort for the realization of new customer changes can be estimated and added to the budget. The correction changes also cause huge efforts, which can lead to a negative budget in the project which is a big challenge for the project management, the automated calculation of effort estimations for the complete development life-cycle. This thesis offers a new model to improve the effort estimation from multiple perspectives. This model also integrates follow-up-defects in later process phases. Thus, the defect cost flow is part of the model and enables the management defects and follow-up defects which could spread throughout the development phases. The newly developed model was successfully evaluated in the automotive domain. The overall accuracy of the effort estimations was improved by 80%

Enhancing Software Project Outcomes: Using Machine Learning and Open Source Data to Employ Software Project Performance Determinants

Many factors can influence the ongoing management and execution of technology projects. Some of these elements are known a priori during the project planning phase. Others require real-time data gathering and analysis throughout the lifetime of a project. These real-time project data elements are often neglected, misclassified, or otherwise misinterpreted during the project execution phase resulting in increased risk of delays, quality issues, and missed business opportunities. The overarching motivation for this research endeavor is to offer reliable improvements in software technology management and delivery. The primary purpose is to discover and analyze the impact, role, and level of influence of various project related data on the ongoing management of technology projects. The study leverages open source data regarding software performance attributes. The goal is to temper the subjectivity currently used by project managers (PMs) with quantifiable measures when assessing project execution progress. Modern-day PMs who manage software development projects are charged with an arduous task. Often, they obtain their inputs from technical leads who tend to be significantly more technical. When assessing software projects, PMs perform their role subject to the limitations of their capabilities and competencies. PMs are required to contend with the stresses of the business environment, the policies, and procedures dictated by their organizations, and resource constraints. The second purpose of this research study is to propose methods by which conventional project assessment processes can be enhanced using quantitative methods that utilize real-time project execution data. Transferability of academic research to industry application is specifically addressed vis-à-vis a delivery framework to provide meaningful data to industry practitioners

The use of Bayesian networks to determine software inspection process efficiency

Adherence to a defined process or standards is necessary to achieve satisfactory software quality. However, in order to judge whether practices are effective at achieving the required integrity of a software product, a measurement-based approach to the correctness of the software development is required. A defined and measurable process is a requirement for producing safe software productively. In this study the contribution of quality assurance to the software development process, and in particular the contribution that software inspections make to produce satisfactory software products, is addressed. I have defined a new model of software inspection effectiveness, which uses a Bayesian Belief Network to combine both subjective and objective data to evaluate the probability of an effective software inspection. Its performance shows an improvement over the existing published models of inspection effectiveness. These previous models made questionable assumptions over the distribution of errors and were essentially static. They could not make use of experience both in terms of process improvement and the increased experience of the inspectors. A sensitivity analysis of my model showed that it is consistent with the attributes which were thought important by Michael Fagan in his research into the software inspection method. The performance of my model show that it is an improvement over published models and over a multiple logistic regression model, which was formed using the same calibration data. By applying my model of software inspection effectiveness before the inspection takes place, project managers will be able to make better use of inspection resource available. Applying the model using data collected during the inspection will help in estimation of residual errors in a product. Decisions can then be made if further investigations are required to identify errors. The modelling process has been used successfully in an industrial application

Predicting deterioration rate of culvert structures utilizing a Markov model

A culvert is typically a hydraulic passage, normally placed perpendicular to the road alignment, which connects the upstream and downstream sections underneath an embankment, while also providing structural support for earth and traffic loads. The structural condition of culverts continues to deteriorate due to aging, limited maintenance budgets, and increased traffic loads. Maintaining the performance of culverts at acceptable levels is a priority for the U.S. Department of Transportation (DOT), and an effective maintenance of culvert structures can be greatly improved by introducing asset management practices. A priority list generated by traditional condition assessment might not provide optimum solutions, and benefits of culvert asset management practices can be maximized by incorporating prediction of deterioration trends. This dissertation includes the development of a decision making chart for culvert inspection, the development of a culvert rating methodology using the Analytic Hierarchy Process (AFIP) based on an expert opinion survey and the development of a Markovian model to predict the deterioration rate of culvert structures at the network level. The literature review is presented in three parts: culvert asset management systems in the U.S.; Non-destructive Technologies (NDT) for culvert inspection (concrete, metal, and thermoplastic culvert structures); and statistical approaches for estimating the deterioration rate for infrastructure. A review of available NDT methods was performed to identify methods applicable for culvert inspection. To identify practices currently used for culvert asset management, culvert inventory data requests were sent to 34 DOTs. The responses revealed that a relatively small number of DOTs manage their culvert assets using formal asset management systems and, while a number of DOTs have inventory databases, many do not have a methodology in place to convert them to priority lists. In addition, when making decisions, DOTs do not incorporate future deterioration rate information into the decision making process. The objective of this work was to narrow the gap between research and application. The culvert inventory database provides basic information support for culvert asset management. Preliminary data analysis of datasets provided by selected DOTs was performed to demonstrate the differences among them. An expert opinion survey using AHP was performed to confirm the weight of 23 factors, which was believed to contribute to the hydraulic & structural performance of culvert structures, so as to establish the culvert rating methodology. A homogenous Markov model, which was calibrated using the Metropolis-Hastings Algorithm, was utilized in the computation of the deterioration rate of culverts at the network level. A real world case study consisting of datasets of three highways inspected regularly by Oregon DOT is also presented. The performance of the model was validated using Pearson\u27s chi-square test