A FRAMEWORK FOR SOFTWARE RELIABILITY MANAGEMENT BASED ON THE SOFTWARE DEVELOPMENT PROFILE MODEL

Recent empirical studies of software have shown a strong correlation between change history of files and their fault-proneness. Statistical data analysis techniques, such as regression analysis, have been applied to validate this finding. While these regression-based models show a correlation between selected software attributes and defect-proneness, in most cases, they are inadequate in terms of demonstrating causality. For this reason, we introduce the Software Development Profile Model (SDPM) as a causal model for identifying defect-prone software artifacts based on their change history and software development activities. The SDPM is based on the assumption that human error during software development is the sole cause for defects leading to software failures. The SDPM assumes that when a software construct is touched, it has a chance to become defective. Software development activities such as inspection, testing, and rework further affect the remaining number of software defects. Under this assumption, the SDPM estimates the defect content of software artifacts based on software change history and software development activities. SDPM is an improvement over existing defect estimation models because it not only uses evidence from current project to estimate defect content, it also allows software managers to manage software projects quantitatively by making risk informed decisions early in software development life cycle. We apply the SDPM in several real life software development projects, showing how it is used and analyzing its accuracy in predicting defect-prone files and compare the results with the Poisson regression model

Mechanical Properties of Microstructural Components of Inorganic Materials

Disertační práce se zabývá studiem strukturních a mechanických vlastností anorganických materiálů. Cílem je nalezení jednotlivých fází ve zkoumaném materiálu a hlavně lokalizace (mechanicky) nejslabšího místa, jeho ovlivnění a následně výroba materiálu o lepších mechanických vlastnostech. Z důvodu velkého množství použitých metod je základní teorie vložena vždy na začátku příslušné kapitoly. Taktéž z důvodu značného množství výsledků jsou na konci kapitol uvedeny dílčí závěry. Práce je rozdělena na tři části, kdy první se zabývá seznámením s možnostmi modelování mikro-mechanických vlastností a provedením experimentů umožňujících posouzení rozsahu platnosti některého modelu. V druhé části je provedeno shrnutí současných možností indentačních zkoušek pro měření mechanických vlastností strukturních složek betonu a praktické zvládnutí metodiky vhodné k užití pro výzkum materiálů zkoumaných domovským pracovištěm. V třetí části je navržena metoda identifikace nejslabších článků struktury anorganických pojiv a její ověření na konkrétním materiálu zkoumaném na domovském pracovišti. V této dizertační práci jsou použity tyto metody: kalorimetrie, ultrazvukové testování, jednoosá pevnost v tlaku, nanoindentace, korelativní mikroskopie a rastrovací elektronová mikroskopie s energiově disperzním spektrometrem. Dílčími výsledky jsou kompletní charakterizace cementových materiálů, upřesnění stávajících poznatků a nalezení optimálního postupu pro charakterizaci. Hlavním výsledkem je inovativní přístup vedoucí k pozitivnímu ovlivnění materiálu.The doctoral thesis deals with study of structural and mechanical properties of inorganic materials. Goal is to find the weakest (mechanically) phases and interfaces of material. By affecting these structures it should be possible consequently produce a material with better mechanical properties. Due to the large amount of used methods the basic theory is discussed always in the beginning of relevant chapter. Similarly, due to the considerable amount of results every chapter includes partial conclusions. The work is divided in three parts. The first deals with the introduction of the possibilities of modeling micro-mechanical properties and performing of experiments that allow assessment of the scope of some model. In second part itis performed an overview of current possibilities of indentation tests for measuring mechanical properties of structural components of concrete and the practical managing of methods suitable for use for materials research examined at our faculty. In third part the method of identifying the weakest points in structure of inorganic binders is proposed and validation on the particular material examined at our faculty is performed. The methods used in this doctoral thesis are: calorimetry, ultrasonic testing, uniaxial compression, nanoindentation, correlative microscopy and scanning electron microscopy with energy dispersive spectrometer. Partial results are a complete characterization of cementitious materials, specification of existing knowledge and finding the optimal procedure for characterization. The main result is an innovative approach that leads to a positive effect on the material.

Functional requirements for onboard management of space shuttle consumables, volume 1

A study was conducted to determine the functional requirements for onboard management of space shuttle consumables. A generalized consumable management concept was developed for application to advanced spacecraft. The subsystems and related consumables selected for inclusion in the consumables management system are: (1) propulsion, (2) power generation, and (3) environmental and life support

Optimized Deeplearning Algorithm for Software Defects Prediction

Accurate software defect prediction (SDP) helps to enhance the quality of the software by identifying potential flaws early in the development process. However, existing approaches face challenges in achieving reliable predictions. To address this, a novel approach is proposed that combines a two-tier-deep learning framework. The proposed work includes four major phases:(a) pre-processing, (b) Dimensionality reduction, (c) Feature Extraction and (d) Two-fold deep learning-based SDP. The collected raw data is initially pre-processed using a data cleaning approach (handling null values and missing data) and a Decimal scaling normalisation approach. The dimensions of the pre-processed data are reduced using the newly developed Incremental Covariance Principal Component Analysis (ICPCA), and this approach aids in solving the “curse of dimensionality” issue. Then, onto the dimensionally reduced data, the feature extraction is performed using statistical features (standard deviation, skewness, variance, and kurtosis), Mutual information (MI), and Conditional entropy (CE). From the extracted features, the relevant ones are selected using the new Euclidean Distance with Mean Absolute Deviation (ED-MAD). Finally, the SDP (decision making) is carried out using the optimized Two-Fold Deep Learning Framework (O-TFDLF), which encapsulates the RBFN and optimized MLP, respectively. The weight of MLP is fine-tuned using the new Levy Flight Cat Mouse Optimisation (LCMO) method to improve the model's prediction accuracy. The final detected outcome (forecasting the presence/ absence of defect) is acquired from optimized MLP. The implementation has been performed using the MATLAB software. By using certain performance metrics such as Sensitivity, Accuracy, Precision, Specificity and MSE the proposed model’s performance is compared to that of existing models. The accuracy achieved for the proposed model is 93.37%

Analyze the Performance of Software by Machine Learning Methods for Fault Prediction Techniques

Trend of using the software in daily life is increasing day by day. Software system development is growing more difficult as these technologies are integrated into daily life. Therefore, creating highly effective software is a significant difficulty. The quality of any software system continues to be the most important element among all the required characteristics. Nearly one-third of the total cost of software development goes toward testing. Therefore, it is always advantageous to find a software bug early in the software development process because if it is not found early, it will drive up the cost of the software development. This type of issue is intended to be resolved via software fault prediction. There is always a need for a better and enhanced prediction model in order to forecast the fault before the real testing and so reduce the flaws in the time and expense of software projects. The various machine learning techniques for classifying software bugs are discussed in this paper