In the planning of a software development project, a major challenge faced by project managers is to predict the rework effort. (Rework effort is the effort required to fix the software defects identified during system testing). The project manager's objective is to deliver the software within the time, cost and quality requirements given by the client. To ensure the quality of the software, many testing cycles will be conducted before it is finally delivered to the client for acceptance. Each testing cycle is a costly affair as it involves running all possible test scenarios in all possible environments, followed by defect fixing and re-verification of defect fixes. On average, two to three testing cycles are conducted but this depends on the number of defects identified during testing. The number of defects will depend on the team expertise and whether they earlier worked on similar projects and technologies. Therefore, it becomes critical to predict the number of defects that will be identified during testing and it is a very challenging task as it requires a good model to predict the rework effort. In this paper, we describe the relationships among software size, number of software defects, productivity and efforts for web-based development projects. These relationships are established by using the multiple linear regression technique on the benchmarking data published by International Software Benchmarking Standard Group. Results suggest that in web-based projects the number of defects identified is directly proportional to the productivity, i.e. higher productivity will led to more defects found and lower productivity will lead to fewer defects found, therefore, less testing and rework effort will be required if project is planned with lower productivity because we can spend more time on development (i.e. time spend till construction phase) to reduce the number of defects found during testing and it will directly contribute in reducing the rework efforts. We infer from the relationship that software size has a significant impact on the total number of defect identified. We also infer that while planning a software project we should use appropriate tools to reduce the margin of error in size estimation and we should also re-estimate the size after every phase of the development life cycle to re-calibrate overall efforts and to minimize the impact on the project plan.
Every software development project is unique and different from repeatable manufacturing process. Each software project share different challenges related to technology, people and timelines. If every project is unique, how project manager can estimate project in a consistent way by applying his past experience. One of the major challenges faced by the project manager is to identify the key software metrics to control and monitor the project execution. Each software development project may be unique but share some common metric that can be used to control and monitor the project execution. These metrics are software size, effort, project duration and productivity. These metrics tells project manager about what to deliver (size), how it was delivered in past (productivity) and how long will it take to deliver with current team capability (time and effort). In this paper, we explain the relationship among these key metrics and how they statistically impact each other. These relationships have been derived based on the data published in book "Practical Software Estimation" by International Software Benchmarking Group. This paper also explains how these metrics can be used in predicting the total number of defects. Study suggests that out of the four key software metrics software size significantly impact the other three metrics (project effort, duration and productivity). Productivity does not significantly depend on the software size but it represents the nonlinear relationship with software size and maximum team size, hence, it is recommended not to have a very big team size as it might impact the overall productivity. Total project duration only depends on the software size and it does not depend on the maximum team size. It implies that we cannot reduce project duration by increasing the team size. This fact is contrary to the perception that we can reduce the project duration by increasing the project team size. We can conclude that software size is the important metrics and a significant effort must be put during project initiation phases to estimate the project size. As software size will help in estimating the project duration and project efforts so error in estimating the software size will have significant impact on the accuracy of project duration and effort. All these key metrics must be re-calibrated during the project development life cycle.
<p class="MsoNormal" style="margin: 0in 0in 10pt; text-align: justify;"><span style="line-height: 115%; font-size: 9pt; mso-bidi-font-size: 12.0pt;"><span style="font-family: Calibri;"><span style="mso-spacerun: yes;"><span style="font-size: small;"><span style="line-height: 115%; mso-bidi-font-size: 12.0pt;">Every software development project is unique and different from repeatable manufacturing process. Each software project share different challenges related to technology, people and timelines. If every project is unique, how project manager can estimate project in a consistent way by applying his past experience. One of the major challenges faced by the project manager is to identify the key software metrics to control and monitor the project execution. Each software development project may be unique but share some common metric that can be used to control and monitor the project execution. These metrics are software size, effort, project duration and productivity. These metrics tells project manager about what to deliver (size), how it was delivered in past (productivity) and how long will it take to deliver with current team capability (time and effort). In this paper, we explain the relationship among these key metrics and how they statistically impact each other. These relationships have been derived based on the data published in book “Practical Software Estimation” by International Software Benchmarking Group. This paper also explains how these metrics can be used in predicting the total number of defects. Study suggests that out of the four key software metrics software size significantly impact the other three metrics (project effort, duration and productivity). Productivity does not significantly depend on the software size but it represents the nonlinear relationship with software size and maximum team size, hence, it is recommended not to have a very big team size as it might impact the overall productivity. Total project duration only depends on the software size and it does not depend on the maximum team size. It implies that we cannot reduce project duration by increasing the team size. This fact is contrary to the perception that we can reduce the project duration by increasing the project team size. We can conclude that software size is the important metrics and a significant effort must be put during project initiation phases to estimate the project size. As software size will help in estimating the project duration and project efforts so error in estimating the software size will have significant impact on the accuracy of project duration and effort. All these key metrics must be re-calibrated during the project development life cycle. </span><strong style="mso-bidi-font-weight: normal;"></strong></span></span></span></span></p><p class="MsoNormal" style="margin: 0in 0in 10pt; text-align: justify;"> </p>
<p>To make a perfect project plan, the software size of the order from the customer is the most important factor. The biggest challenge for the project manager is to estimate the project end date in the beginning of the project i.e. in project planning phase with realistic accuracy. Apart from other major inputs to estimate the project end date, expected team capability (productivity) and estimated software size are the major inputs that may influence the project end date. Software size is one of the most significant independent metric available in the planning phase and project manager has to estimate the other metrics based on the initial estimated software size. There is no direct relationship available between software size and project duration or software size and team productivity, however, there are industry data published by Quantitative Software Management and ISBSG that shows how these metrics influence each other. In this paper, using the data published by ISBSG and Quantitative Software Management, we try to statistically establish how productivity and project duration are influenced by software size. We have done linear regression analysis by generating the secondary data based on the data published by ISBSG and Quantitative Software Management. Linear regression equation validated with the actual project data and experimental results suggest that that productivity is significantly dependent on software size, however, project duration does not significantly depend on software size but may also be dependent on other metrics like team size, apart from software size.</p>
Three technological aspects have a significant impact on the functioning of an optimal stent. The substance it is made up of, model or design, and coating of the surface are important areas for research. To give recognition of an ideal stent, it summarizes some essential breakthroughs that occurred. Encrustation is a regular problem that can happen when a ureteral stent is implanted in the urinary tract, and it may be dangerous. The part of the paper covers the mechanism of encrustation, stent management, and the most recent technologies created to solve this problem. Encrustation has a complicated and diverse mechanism that includes the time it stays inside, patient-specific risk factors, controlled film production, formation of biofilm, and deposition of minerals. A number of high-tech advancements in stent substances and coverings/coatings could help to reduce the danger of encrustation of stents. It's critical to determine the amount of encrustation of a stent so that therapy options can be tailored properly. For the care of ureteral stents, which are encrusted, we offer a unique therapeutic protocol. The duration of stent indwelling time has been repeatedly established to be a critical risk factor for the evolution of encrustation. The period of stent indwelling time has consistently been established to be a critical risk element for the evolution of encrustation. Patients who are predisposed to bacteriuria and urinary lithiasis are also predisposed to encrustation. Repeated urinary tract infections, diabetes, and chronic kidney failure are among the factors that might escalate urine bacterial load, which can lead to stent encrustation. Due to the prevalence of ureteral stents in urology, it's critical to keep up to date on the best ways to prevent stent encrustation, recognize high-risk patients, and remove them using multimodal techniques.
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