Prashant Johri

Flexible Discrete Software Reliability Growth Model for Distributed Environment Incorporating Two Types of Imperfect Debugging

In literature we have several software reliability growth models developed to monitor the reliability growth during the testing phase of the software development. These models typically use the calendar / execution time and hence are known as continuous time SRGM. However, very little seems to have been done in the literature to develop discrete SRGM. Discrete SRGM uses test cases in computer test runs as a unit of testing. Debugging process is usually imperfect because during testing all software faults are not completely removed as they are difficult to locate or new faults might be introduced. In real software development environment, the number of failures observed need not be same as the number of errors removed. If the number of failures observed is more than the number of faults removed then we have the case of imperfect debugging. Due to the complexity of the software system and the incomplete understanding of the software requirements, specifications and structure, the testing team may not be able to remove the fault perfectly on detection of the failure and the original fault may remain or get replaced by another fault. In this paper, we discuss a discrete software reliability growth model for distributed system considering imperfect debugging that faults are not always corrected/removed when they are detected and fault generation. The proposed model assumes that the software system consists of a finite number of reused and newly developed sub-systems. The reused sub-systems do not involve the effect of severity of the faults on the software reliability growth phenomenon because they stabilize over a period of time i.e. the growth is uniform whereas, the newly developed subsystem does involve. For newly developed component, it is assumed that removal process follows logistic growth curve due to the fact that learning of removal team grows as testing progresses. The fault removal phenomena for reused and newly developed sub-systems have been modeled separately and are summed to obtain the total fault removal phenomenon of the software system. The model has been validated on two software data sets and it is shown that the proposed model fairs comparatively better than the existing one.

Review paper on text and audio steganography using GA

Steganography is used to hide the secret information within a cover media in such a way that the existence of the message could not be noticeable. Here we are considering audio file as cover media and text message as secret information. The secret information is embedded in a cover media as noise as the HAS cannot detect the sound less than 20Hz or greater than 20000Hz. Generally LSB algorithm is used to embed the secret information within a cover media. Here we are using genetic programming to increase the robustness of the data so that the secret data could not be noticeable as far as possible.

Review on Software Reliability Growth Models and Software Release Planning

paper, Software Reliability Engineering is a field that developed from ancestry in the reliability disciplines of structural, electrical, and hardware engineering. Reliability models are powerful tools of Software Reliability Engineering for estimating, predicting, devious, and assessing software reliability. On the basis of the review the cataloging of software reliability models has been presented as a major part. This categorization is based on the various dimensions of reliability models. Models under review reflect either infinite or finite number of failures. This paper discusses a two- dimensional software reliability growth modeling framework. We measured that an actual software reliability growth progression depends not only on testing time but also on testing effort and also enables us to portray software release planning problem in software reliability growth process. Thus, we can say that software project managers can demeanor more viable and accurate software reliability appraisal by using two-dimensional SRGM.

A Genetic Algorithm Approach for Optimal Allocation of Software Testing Effort

Allocation of limited testing efforts to a software development project is a complex task for software managers. The challenges become difficult when the nature of the development process is considered in the dynamic environment. Numerous software reliability growth models have been proposed in last one decade to minimize the whole testing effort expenditures, but generally under static assumption. The main purpose of this article is to distribute total testing resource optimally under dynamic condition. An elaborate optimization policy is proposed using genetic algorithm and numerical example is also demonstrated. Genetic Algorithms (GAs) works with a set of individuals, representing probable solutions of the task. The selection theory is applied by using a criterion, giving an evaluation for the individual with respect to the desired solution. This article also studies the optimal resource allocation problems for different conditions by investigative the activities of the model parameters.

Testing-effort-dependent software reliability growth model for a distributed environment using debugging time lag functions

Distributed software system consists of reused and newly developed software components. This paper presents testing-effort-dependent Software Reliability Growth Model (SRGM) for distributed environment. We have used logistic learning function for newly developed components with the assumption that the skill of the testing team grows as testing progresses. We assume that detected faults are not immediately removed but lag the fault detection process by debugging time lag. Thus, different debugging time lag functions have been used during removal process for newly developed components. The proposed model has been validated and is shown that the proposed model results are comparatively better than the existing ones.

Open Source Software Reliability Growth Models for Distributed Environment Based on Component-Specific Testing-Efforts

Because of availability, redistributable, affordability, modifiability, of source code, free and no restriction in choice, open source is a favorite platform for lot of software industries and peoples, who consider using the power of extremely reliable and superior quality software. Numeouus SRGMs have been proposed to estimate the reliability of the software of OSSs; however, no one has proven to perform very well considering diverse project characteristics. In the models for OSSs, the error deletion experience for the reused and the newly developed components based on component-specific testing-effort is demonstrated. It is considered that there are several different types of faults for newly developed component and single type of faults for reused components for obtaining the unambiguous expressions for the mean number of individual types of errors. For OSSs system components testing-efforts have to be modeled separately for each and every component in the system. The total effort of the system is then calculated from the summation of component-specific testing-effort functions. We have employed MATLAB as implementation framework for performing all the estimations. Our approach partitions the testing effort with growth curves of varying nature among different components of the same OSS. To validate our analytical results, numerical illustrations have also been provided.

Use case point estimation technique in software development

Software Projects are developed with the prior requirements and should be capable to complete on time under a fixed budget but it gets late to delivered, gets over-budget and even not able to meet user expectations. In agile approach, the estimation of software depends on expert opinion or on any historical data which is used as the input to previous methods like planning poker. The accuracy in estimation is the primary goal of any development but various factors related to environment and technical complexity which may further alleviate the size and effort of a project. Previously proposed estimation models were successful in estimation but lacks due to some obstacles such as less accuracy and customer satisfaction as per the requirement, other factors such as complexity, risk tracking and estimation. This paper emphasizes on a new algorithmic approach to estimate considering Environment and Technical factors so as to have a more accuracy with the use cases under agile development.

Testing Resource Allocation for Fault Detection Process

Developing quality software is one of the most challenging tasks, for developing quality software we have to remove the entire bug from software before the software switch into operational phase. For this we have to allocate our testing and debugging resource based on the time so that we can finish off our work. For distributing the testing and debugging resource we are using software reliability growth model (SRGMs). Numerous SRGMs has been developed in past couple of decade for allocating the testing and debugging resource but mostly under static condition. In this article we developed a mathematical model for allocating the resource in dynamic environment. In this article we utilized Pontryagin maximum principle for illuminating the model. Finally one numerical illustration is explained for distributing the software testing resource for created module. Here Genetic Algorithm (GA) is used for allocating resource optimally.

TESTING AND DEBUGGING RESOURCE ALLOCATION FOR FAULT DETECTION AND REMOVAL PROCESS

In software development life cycle (SDLC) testing is very important step. One of the key elements of software quality is testing. Fault detection and removal process is also very important when we are doing testing. In the last 30 years numerous software reliability growth models where developed for fault detection and correction process. Majority of models where developed under static condition. The main goal of this article is to examine the resource allocation plan for fault detection and correction process of the software to control the cost during testing and operational phase. For achieving this we developed a model for fault detection and correction process. For solving this model we use Pontryagain’s Maximum principle. For optimally resource allocation we use Differential Evolution (DE). Differential Evolution (DE) is an optimization algorithm. A numerical example is also explained for resource allocation for fault detection and removal process.

Smart Gesture Control for Home Automation Using Internet of Things

Internet of Things (IoT) is a system where the machines involved in a system or an infrastructure can be monitored and certain activities can be controlled with the help of sensors and actuators. This research presents the model for controlling remote devices with the help of hand gestures. The gestures are recognized using template matching algorithms to identify the hand gestures and control remote things accordingly using the Internet of Things. The microcontrollers and the remote units which are involved in the architecture are connected to the Internet either via LAN or Wi-Fi module. The proposed system will help those who generally forget to switch off the power when not in use, as an example. It is a contribution to the body of knowledge in the field of home automation using a microcontroller.