Software fault prediction using Nonlinear Autoregressive with eXogenous Inputs (NARX) network

This paper explores a new approach for predicting software faults by means of NARX neural network. Also, a careful analysis has been carried out to determine the applicability of NARX network in software reliability. The validation of the proposed approach has been performed using two real software failure data sets. Comparison has been made with some existing parametric software reliability models as well as some neural network (Elman net and TDNN) based SRGM. The results computed shows that the proposed approach outperformed the other existing parametric and neural network based software reliability models with a reasonably good predictive accuracy.     

Synthesizing robust systems

Systems should not only be correct but also robust in the sense that they behave reasonably in unexpected situations. This article addresses synthesis of robust reactive systems from temporal specifications. Existing methods allow arbitrary behavior if assumptions in the specification are violated. To overcome this, we define two robustness notions, combine them, and show how to enforce them in synthesis. The first notion applies to safety properties: If safety assumptions are violated temporarily, we require that the system recovers to normal operation with as few errors as possible. The second notion requires that, if liveness assumptions are violated, as many guarantees as possible should be fulfilled nevertheless. We present a synthesis procedure achieving this for the important class of GR(1) specifications, and establish complexity bounds. We also present an implementation of a special case of robustness, and show experimental results.     

A fuzzy rule-based generation algorithm in interval type-2 fuzzy logic system for fault prediction in the early phase of software development

Reliability, a measure of software, deals in total number of faults count up to a certain period of time. The present study aims at estimating the total number of software faults during the early phase of software life cycle. Such estimation helps in producing more reliable software as there may be a scope to take necessary corrective actions for improving the reliability within optimum time and cost by the software developers. The proposed interval type-2 fuzzy logic-based model considers reliability-relevant software metric and earlier project data as model inputs. Type-2 fuzzy sets have been used to reduce uncertainties in the vague linguistic values of the software metrics. A rule formation algorithm has been developed to overcome inconsistency in the consequent parts of large number of rules. Twenty-six software project data help to validate the model, and a comparison has been provided to analyse the proposed model’s performance.     

Recursive Estimation of Motion Parameters

A robust algroithm that estimates the motion parameters recursively from a sequence of noisy images is presented here. We propose the use of the least median of squares method in conjunction with a computationally efficient recursive scheme. The method works well even when nearly half of the features have been matched very poorly. A recursive constrained least squares method is developed while dealing with a range or stereo data sequence and a recursive total least squares method is proposed for the monocular data sequence.     

Emotion Recognition From Facial Expressions and Its Control Using Fuzzy Logic

This paper presents a fuzzy relational approach to human emotion recognition from facial expressions and its control. The proposed scheme uses external stimulus to excite specific emotions in human subjects whose facial expressions are analyzed by segmenting and localizing the individual frames into regions of interest. Selected facial features such as eye opening, mouth opening, and the length of eyebrow constriction are extracted from the localized regions, fuzzified, and mapped onto an emotion space by employing Mamdani-type relational models. A scheme for the validation of the system parameters is also presented. This paper also provides a fuzzy scheme for controlling the transition of emotion dynamics toward a desired state. Experimental results and computer simulations indicate that the proposed scheme for emotion recognition and control is simple and robust, with good accuracy.     

Estimation of impact forces during multi-point collisions involving small deformations

Multibody System Dynamics - Collision between hard objects causes abrupt changes in the velocities of the system, which are characterized by very large contact forces over very small time...     

Flow of materials in rotary kilns used for sponge iron manufacture: Part I. Effect of some operational variables

Looking forward to the need of developing coal-based sponge iron technology in India, a country having no significant resources of either coking coal or natural gas, the Research and Development Division of the Tata Iron and Steel Company Limited (TISCO) set up a rotary kiln based direct reduction pilot plant in 1975. In this pilot plant, a totally indigenous technology for production of sponge iron has been developed in which non-coking coal is essentially used as the reductant. For easy scaling up of the TISCO Direct Reduction (TDR) process to units of 300 to 400 tpd capacity, it was thought necessary to explore some of the fundamental aspects of material flow in a rotary kiln. This was carried out by studying the flow of materials in room temperature models. The work was divided into two parts: first, a study of the influence of several operating parameters,viz., rotational speed, inclination of the kiln, effect of circular dams at the feed and the exit ends of the kiln,etc., and second, an investigation of the extent of segregation of a mixture of solids in a rotating kiln. The highlights of the experimental results dealing mainly with the effect of various kiln operating variables on the filling degree profile of the charge in the kiln are presented. Formerly Joint Director, Research and Development, TISCO Formerly with TISCO, Jamshedpur     

A new fuzzy rule based algorithm for estimating software faults in early phase of development

Estimation of reliability and the number of faults present in software in its early development phase, i.e., requirement analysis or design phase is very beneficial for developing reliable software with optimal cost. Software reliability prediction in early phase of development is highly desirable to the stake holders, software developers, managers and end users. Since, the failure data are unavailable in early phase of software development, different reliability relevant software metrics and similar project data are used to develop models for early software fault prediction. The proposed model uses the linguistic values of software metrics in fuzzy inference system to predict the total number of faults present in software in its requirement analysis phase. Considering specific target reliability, weightage of each input software metrics and size of software, an algorithm has been proposed here for developing general fuzzy rule base. For model validation of the proposed model, 20 real software project data have been used here. The linguistic values from four software metrics related to requirement analysis phase have been considered as model inputs. The performance of the proposed model has been compared with two existing early software fault prediction models.     

A Branching Time Temporal Framework for Quantitative Reasoning

Temporal logics such as Computation Tree Logic (CTL) and Linear Temporal Logic (LTL) have become popular for specifying temporal properties over a wide variety of planning and verification problems. In this paper we work towards building a generalized framework for automated reasoning based on temporal logics. We present a powerful extension of CTL with first-order quantification over the set of reachable states for reasoning about extremal properties of weighted labeled transition systems in general. The proposed logic, which we call Weighted Quantified Computation Tree Logic (WQCTL), captures the essential elements common to the domain of planning and verification problems and can thereby be used as an effective specification language in both domains. We show that in spite of the rich, expressive power of the logic, we are able to evaluate WQCTL formulas in time polynomial in the size of the state space times the length of the formula. Wepresent experimental results on the WQCTL verifier.     

A bayesian belief network based model for predicting software faults in early phase of software development process

It is always better to have an idea about the future situation of a present work. Prediction of software faults in the early phase of software development life cycle can facilitate to the software personnel to achieve their desired software product. Early prediction is of great importance for optimizing the development cost of a software project. The present study proposes a methodology based on Bayesian belief network, developed to predict total number of faults and to reach a target value of total number of faults during early development phase of software lifecycle. The model has been carried out using the information from similar or earlier version software projects, domain expert’s opinion and the software metrics. Interval type-2 fuzzy logic has been applied for obtaining the conditional probability values in the node probability tables of the belief network. The output pattern corresponding to the total number of faults has been identified by artificial neural network using the input pattern from similar or earlier project data. The proposed Bayesian framework facilitates software personnel to gain the required information about software metrics at early phase for achieving targeted number of software faults. The proposed model has been applied on twenty six software project data. Results have been validated by different statistical comparison criterion. The performance of the proposed approach has been compared with some existing early fault prediction models.