Towards an Improved Software Project Monitoring Task Model of Agile Kanban Method

Agile Kanban method recently is gaining increasing attention and popularity in software development organizations (SDOs). This method has numerous advantages that make it performs better than other Agile methods in terms of managing software projects. However, different studies revealed that this method has significant challenges that negatively impact the scheduling of the development process. Therefore, late delivery of software projects may occur, thus the rate of projects failures will be increased. In response, this paper aims to explicate the current challenges in progress monitoring task of Agile Kanban method. Accordingly, the results gave insights to bridge that gap by developing an improved software project monitoring task model of Agile Kanban method. To do so, we identified the components and criteria that affect software project monitoring task, and then an initial model has proposed. The initial model consists of three main components, which are (1) extending progress tracking, (2) generating optimum WIP limits, and (3) visualizing useful insights for workflow. Further research can be focused on developing and evaluating the proposed model through discussion with the knowledge and domain experts

Short Production Run Control Charts to Monitor Process Variances

Control chart is one of the most commonly used statistical tools for quality control and improvement. If the process mean and standard deviation are not given or unknown, most Shewhart control charts require sufficient sample data before the control chart can be established. However, in certain industries or processes, it may not be practical to collect adequate amount of data at the beginning of the manufacturing process to build the trial control chart in Phase I. For quality improvement in such or similar processes, some authors developed self-starting control charts for short-run production, e.g. t chart, Q chart, EWMA t chart/Q chart, CUSUM t chart/Q chart. This thesis studies the performance of some short run control charts for monitoring process variances. Numerical simulations are using in this study. The results of the numerical experiments are extensively tested for different combinations of process lengths and starting points of process shifts

Methods, Techniques and Tools to Support Software Project Management in High Maturity

High maturity in software development is associated to statistical control of critical subprocesses performance and the use of gained predictability to manage projects with better planning precision and monitoring control. Although maturity models such as CMMI mention some statistical and other quantitative approaches, methods and techniques that can support project management in high maturity, they do not provide details about them neither present their available types. Therefore, there is a lack of knowledge on how to support software process improvement initiatives to choose and use statistical and other quantitative approaches, methods and techniques on such context. The objective of this study is to identify different approaches, methods and techniques that can assist on managing projects in high maturity. By conducting a systematic literature mapping on major data sources, we identified 75 papers describing 101 contributions. We briefly describe identified approaches, methods and techniques grouped by similar types and provide some analysis regarding technological maturity stage and evaluation method, and supported development methods and characteristics and process/indicator area in which they were applied. We hope this information can fill some of the statistical and quantitative knowledge gap about the actual types of approaches, methods and techniques being proposed, evaluated, experimented and adopted by organizations to assist on quantitative project management in high maturity