HV Process Model of Software Development

Software Development Life Cycle (SDLC) is one of the major ingredients for the development of efficient software systems within a time frame and low-cost involvement. From the literature, it is evident that there are various kinds of process models that are used by the software industries for the development of small, medium and long-term software projects, but many of them do not cover risk management. It is quite obvious that the improper selection of the software development process model leads to failure of the software products as it is time bound activity. In the present work, a new software development process model is proposed which covers the risks at any stage of the development of the software product. The model is named a Hemant-Vipin (HV) process model and may be helpful for the software industries for development of the efficient software products and timely delivery at the end of the client. The efficiency of the HV process model is observed by considering various kinds of factors like requirement clarity, user feedback, change agility, predictability, risk identification, practical implementation, customer satisfaction, incremental development, use of ready-made components, quick design, resource organization and many more and found through a case study that the presented approach covers many of parameters in comparison of the existing process models. .

Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

This research recognizes the limitation and challenges of adaptingand applying Process Mining as a powerful tool and technique in theHypothetical Software Architecture (SA) Evaluation Framework with thefeatures and factors of lightweightness. Process mining deals with the largescalecomplexity of security and performance analysis, which are the goalsof SA evaluation frameworks. As a result of these conjectures, all ProcessMining researches in the realm of SA are thoroughly reviewed, and ninechallenges for Process Mining Adaption are recognized. Process mining isembedded in the framework and to boost the quality of the SA model forfurther analysis, the framework nominates architectural discovery algorithmsFlower, Alpha, Integer Linear Programming (ILP), Heuristic, and Inductiveand compares them vs. twelve quality criteria. Finally, the framework’s testingon three case studies approves the feasibility of applying process mining toarchitectural evaluation. The extraction of the SA model is also done by thebest model discovery algorithm, which is selected by intensive benchmarkingin this research. This research presents case studies of SA in service-oriented,Pipe and Filter, and component-based styles, modeled and simulated byHierarchical Colored Petri Net techniques based on the cases’ documentation.Processminingwithin this framework dealswith the system’s log files obtainedfrom SA simulation. Applying process mining is challenging, especially for aSA evaluation framework, as it has not been done yet. The research recognizesthe problems of process mining adaption to a hypothetical lightweightSA evaluation framework and addresses these problems during the solutiondevelopment.

Designing Software to Analyze Sewing Process of Industrial Knitted Products

In the textile industry,garment manufacturing contains four major pro-cesses containing cutting,sewing,finishing,and packaging.Sewing is the most crucial and intricate section,dealing with a large number of varied operations.A successful sewing process needs to be optimized regarding different factors,including time,sewing equipment,and skilled workers.Assembly lineflow is combined by a set of operations with a particular sequence.The utmost impor-tance of all garment industry is to arrange the workstations to minimize the num-ber of employees in order to produce at the best productive rate with the most reasonable cost,shortest time,and satisfying quality.In most garment factories,the production lines are balanced using the empirical judgment of the line man-agers.For the whole process the data of production time at each step,labor pro-ductivity,proper choices of equipment were always needed to calculate line efficiency.As far as the issue is concerned,there has not been an academically sewing process analyzing software providing adequate data of sewing motions and sewing time as the credible input for the line balancing tasks.Towards this goal,this paper presents the results of research on optimizing academically self-built software to analyze the sewing process of knitted products applied to industrial production using Java programming language on Google tools.The results achieved by the software are not only to analyze sewing products and the technological sewing process,calculate the sewing time on the machine but also analyze the sewing activities of workers into manipulations,movements,and motions to calculate the preparation time for two typical knitted products,namely,Polo-Shirt and T-Shirt with the case studies at General Textile Garment Joint Stock Company Hanoi and Star Fashion Company Limited.

A New Signal Processing Technique of π/4-DQPSK Modem Based on Software Radio

DQPSK modem has been chosen as the modem scheme in many mobile communication systems. A new signal processing technique of π/4-DQPSK modem based on software radio is discussed in this paper. Unlike many other software radio solutions to the subject, we choose a universal digital radio baseband processor operating as the co-processor of DSP. Only the core algorithms for signal processing are implemented with DSP. Thus the computation burden on DSP is reduced significantly. Compared with the traditional ones, the technique mentioned in this paper is more promising and attractive. It is extremely compact and power-efficient, which is often required by a mobile communication system. The implementation of baseband signal processing for π/4-DQPSK modem on this platform is illustrated in detail. Special emphases are laid on the architecture of the system and the algorithms used in the baseband signal processing. Finally, some experimental results are presented and the performances of the signal processing and compensation algorithms are evaluated through computer simulations.

Hierarchy-Based Team Software Process Simulation Model

According to the characteristic of Team Software Process （TSP）, it adopts a hierarchy-based model combined discrete event model with system dynamics model. This model represents TSP as form of two levels, the inner level embodies the continuity of the software process, the outer embodies the software development process by phases, and the structure and principle of the model is explained in detail, then formalization description of the model is offered. At last, an example is presented to demonstrate the simulation process and result. This model can simulate team software process from various angles, supervise and predict the software process. Also it can make the management of software development become more scientific and improve the quality of software.

Construction and Practices of the Course of Software Process&Tools Oriented Certified in Engineering Education

In recent years,it is the general trend to adopt the standards of international engineering education certification to construct curriculum.“Software Process and Tools”is one of the core courses of Harbin Institute of Technology’s software engineering undergraduate training program.Focusing on the construction work and practical exploration of the course in the process of reforming the software engineering professional curriculum system,and how to achieve the standards of engineering education certification,This paper makes a review and summary.This paper focuses on the status and the role of the course in the whole curriculum system,as well as project-driven teaching content design and practical teaching methods.And summarizes the experience and results of 3 rounds of teaching practice.

Grey-based approach for estimating software reliability under nonhomogeneous Poisson process

Due to the randomness and time dependence of the factors affecting software reliability, most software reliability models are treated as stochastic processes, and the non-homogeneous Poisson process(NHPP) is the most used one.However, the failure behavior of software does not follow the NHPP in a statistically rigorous manner, and the pure random method might be not enough to describe the software failure behavior. To solve these problems, this paper proposes a new integrated approach that combines stochastic process and grey system theory to describe the failure behavior of software. A grey NHPP software reliability model is put forward in a discrete form, and a grey-based approach for estimating software reliability under the NHPP is proposed as a nonlinear multi-objective programming problem. Finally, four grey NHPP software reliability models are applied to four real datasets, the dynamic R-square and predictive relative error are calculated. Comparing with the original single NHPP software reliability model, it is found that the modeling using the integrated approach has a higher prediction accuracy of software reliability. Therefore, there is the characteristics of grey uncertain information in the NHPP software reliability models, and exploiting the latent grey uncertain information might lead to more accurate software reliability estimation.

Statistical Analysis of Process Monitoring Data for Software Process Improvement and Its Application

Software projects influenced by many human factors generate various risks. In order to develop highly quality software, it is important to respond to these risks reasonably and promptly. In addition, it is not easy for project managers to deal with these risks completely. Therefore, it is essential to manage the process quality by promoting activities of process monitoring and design quality assessment. In this paper, we discuss statistical data analysis for actual project management activities in process monitoring and design quality assessment, and analyze the effects for these software process improvement quantitatively by applying the methods of multivariate analysis. Then, we show how process factors affect the management measures of QCD (Quality, Cost, Delivery) by applying the multiple regression analyses to observed process monitoring data. Further, we quantitatively evaluate the effect by performing design quality assessment based on the principal component analysis and the factor analysis. As a result of analysis, we show that the design quality assessment activities are so effective for software process improvement. Further, based on the result of quantitative project assessment, we discuss the usefulness of process monitoring progress assessment by using a software reliability growth model. This result may enable us to give a useful quantitative measure of product release determination.

One-Sample Bayesian Predictive Analyses for an Exponential Non-Homogeneous Poisson Process in Software Reliability

The Goel-Okumoto software reliability model, also known as the Exponential Nonhomogeneous Poisson Process,is one of the earliest software reliability models to be proposed. From literature, it is evident that most of the study that has been done on the Goel-Okumoto software reliability model is parameter estimation using the MLE method and model fit. It is widely known that predictive analysis is very useful for modifying, debugging and determining when to terminate software development testing process. However, there is a conspicuous absence of literature on both the classical and Bayesian predictive analyses on the model. This paper presents some results about predictive analyses for the Goel-Okumoto software reliability model. Driven by the requirement of highly reliable software used in computers embedded in automotive, mechanical and safety control systems, industrial and quality process control, real-time sensor networks, aircrafts, nuclear reactors among others, we address four issues in single-sample prediction associated closely with software development process. We have adopted Bayesian methods based on non-informative priors to develop explicit solutions to these problems. An example with real data in the form of time between software failures will be used to illustrate the developed methodologies.

Unifying the Software Process Spectrum

Software Process Workshop (SPW 2005) was held in Beijing on May 25-27, 2005. This paper introduces the motivation of organizing such a workshop, as well as its theme and paper gathering and review; and summarizes the main content and insights of 11 keynote speeches, 30 regular papers in five sessions of “Process Content”, “Process Tools and Metrics”, “Process Management”, “Process Representation and Analysis”, and “Experience Reports”, 8 software development support tools demonstration, and the ending panel “Where Are We Now? Where Should We Go Next?”.

A Novel Method for Assessing and Optimizing Software Project Process Based Risk Control

A new approach for assessing and optimizing software project process based on software risk control presented, which evaluates and optimizes software project process from the view of controlling the software project risks. A model for optimizing software risk control is given, a discrete optimization algorithm based on dynamic programruing is proposed and an example of using above method to solve a problem is also included in this paper. By improving the old passive post-project control into an active effective preaction, this new method can greatly promote the possibility of success of software projects.

Software Reliability Growth Model for Imperfect Debugging Process Considering Testing-Effort and Testing Coverage

Because of the inevitable debugging lag,imperfect debugging process is used to replace perfect debugging process in the analysis of software reliability growth model.Considering neither testing-effort nor testing coverage can describe software reliability for imperfect debugging completely,by hybridizing testing-effort with testing coverage under imperfect debugging,this paper proposes a new model named GMW-LO-ID.Under the assumption that the number of faults is proportional to the current number of detected faults,this model combines generalized modified Weibull(GMW)testing-effort function with logistic(LO)testing coverage function,and inherits GMW's amazing flexibility and LO's high fitting precision.Furthermore,the fitting accuracy and predictive power are verified by two series of experiments and we can draw a conclusion that our model fits the actual failure data better and predicts the software future behavior better than other ten traditional models,which only consider one or two points of testing-effort,testing coverage and imperfect debugging.

Component-Based Software Trustworthiness Fuzzy Analytic Hierarchy Process Model

For a more accurate and comprehensive assessment of the trustworthiness of component-based soft- ware system, the fuzzy analytic hierarchy process is introduced to establish the analysis model. Combine qualitative and quantitative analyses, the impacts to overall trustworthiness by the different types of components are distinguished. Considering the coupling relationship between components, dividing the system into several layers from target layer to scheme layer, evaluating the scheme advantages disadvantages by group decision-making, the trustworthiness of a typical J2EE structured component-based software is assessed. The trustworthiness asses model of the software components provides an effective methods of operation.

Process View of Reflection Mechanism for Reuse Software Architecture

Reflection mechanism for reuse software architecture （RMRSA） divides a software architecture into base-level architecture and meta-level architecture logically. Base-level architecture is the ordinary architecture; meta-level represents and manipulates the reusable meta-information of base-level architecture explicitly. Through reflection, the modification of meta-level architecture will result in the modification of the architecture in base-level. Then we can gain a new base-level architecture design. In this paper, we use π-calculus to define the constituents and their interaction processes of RMRSA, by these definition, we specify the business function in base-level at runtime, and illustrate the reflection mechanism between the base-level architecture and meta-level architecture.

What can Software Engineers Learn from Manufacturing to Improve Software Process and Product?

The purpose of this paper is to provide the software engineer with tools from the field of manufacturing as an aid to improving software process and product quality. Process involves classical manufacturing methods, such as statistical quality control applied to product testing, which is designed to monitor and correct the process when the process yields product quality that fails to meet specifications. Product quality is measured by metrics, such as failure count occurring on software during testing. When the process and product quality are out of control, we show what remedial action to take to bring both the process and product under control. NASA Space Shuttle failure data are used to illustrate the process methods.

Two-Sample Bayesian Predictive Analyses for an Exponential Non-Homogeneous Poisson Process in Software Reliability

The Goel-Okumoto software reliability model is one of the earliest attempts to use a non-homogeneous Poisson process to model failure times observed during software test interval. The model is known as exponential NHPP model as it describes exponential software failure curve. Parameter estimation, model fit and predictive analyses based on one sample have been conducted on the Goel-Okumoto software reliability model. However, predictive analyses based on two samples have not been conducted on the model. In two-sample prediction, the parameters and characteristics of the first sample are used to analyze and to make predictions for the second sample. This helps in saving time and resources during the software development process. This paper presents some results about predictive analyses for the Goel-Okumoto software reliability model based on two samples. We have addressed three issues in two-sample prediction associated closely with software development testing process. Bayesian methods based on non-informative priors have been adopted to develop solutions to these issues. The developed methodologies have been illustrated by two sets of software failure data simulated from the Goel-Okumoto software reliability model.

Implementing a Personal Software Process (PSPSM) Course: A Case Study

In order to remain competitive in software industry, software engineers need to continuously improve their proficiency. Personal Software Process (PSPSM) provides a strong concept which helps software engineers inspecting and improving themselves. Yet, when being applied on an undergraduate computer engineering course, several complex mathematical calculations from PSP official exercises could encumber the performance of students who do not possess adequate mathematics background. This paper reports a result of implementing PSP course for undergraduate computer engineering students in Chiang Mai University, Thailand.

Empirical Research on Critical Success Factors of Agile Software Process Improvement

In this paper, we discuss agile software process improvement in P company with their description of process management in current level and analysis of problems, design the P Company success factors model in organizational culture, systems, products, customers, markets, leadership, technology and other key dimensions, which is verified through questionnaire in P company. In the end, we apply knowledge creation theory to analyze the open source software community with successful application of the typical agile software method, propose ten principles of knowledge creation in open source software community: Self-organizing, Code sharing, Adaptation, Usability, Sustention, Talent, Interaction, Collaboration, Happiness, and Democracy.

Lognormal Process Software Reliability Modeling with Testing-Effort

We propose a software reliability growth model with testing-effort based on a continuous-state space stochastic process, such as a lognormal process, and conduct its goodness-of-fit evaluation. We also discuss a parameter estimation method of our model. Then, we derive several software reliability assessment measures by the probability distribution of its solution process, and compare our model with existing continuous-state space software reliability growth models in terms of the mean square error and the Akaike’s information criterion by using actual fault count data.