Quantitative Process Management Research Articles

MaTGeC: hacia un marco de trabajo para la gestión cuantitativa de procesos de desarrollo de software en pequeñas organizaciones

Las organizaciones pequeñas desarrolladoras de software tienen dificultades para adoptar las prác­ticas sobre gestión cuantitativa de procesos ofre­cidas por modelos de referencia como CMMI-DEV o ISO/IEC 15504, debido a que les generan costosas inversiones a largo plazo. Sin embargo, en caso de que no incorporen al menos las prác­ticas esenciales, no podrán obtener procesos con menor variabilidad en su rendimiento y que pue­dan estar bajo control estadístico, lo que afecta de manera directa en el resultado de sus proyectos, ya que permitiría efectuar mejores estimaciones. En este artículo se presenta una propuesta gene­ral de un marco de trabajo para gestión cuanti­tativa de procesos para pequeñas organizaciones (MaTGeC) que ofrece una guía para monitorear y evaluar procesos específicos seleccionados por la organización. Posteriormente, se indican los com­ponentes que lo conforman, junto con las prácti­cas, actividades, roles y productos esenciales para que esta clase de organizaciones incorporen los fundamentos de gestión cuantitativa en sus tareas más críticas.

Panorama of quantitative management of software development process for very small entities

In the software industry, small entities are majority, and is important that incorporate them quantitative processes management practices for to reduce processes variability. CMMI-DEV and ISO/IEC 15504-5 address quantitative management, but are for big organizations. MoProSoft, COMPETISOFT and MPS.BR are for the small enterprises, but not include quantitative management. Through a systematic review of literature, searched for works about quantitative process management for small software developing enterprises indicating what practices do and how to execute. As conclusion, there is not available an integrated proposal that incorporate management practices quantitative of software processes for this kind of organizations.

Apply Quantitative Management Now

The Assessment Approach for Quantitative Process Management (A2QPM) helps identify software process measures for quantitative analysis even when organizations lack formal systems for process measurement. A2QPM is the first approach to quantitative management that offers software organizations a well-defined, detailed guideline for assessing their software processes and applying beneficial quantitative techniques to improve them. All the A2QPM applications we've described resulted in quantitative analysis implementations. Although the organizations had institutionalized neither a measurement process nor the processes that were subject to assessment, A2QPM nevertheless enabled quantitative improvement of the issues under study, such as process performance and product quality. Although we didn't intend the approach to be a shortcut for high ML appraisals, it has also helped organizations on their way to high maturity.

Investigating the effect of variations in the test development process: a case from a safety-critical system

Variation is inherent to a process, and process management demands understanding the nature of variation in quantitative terms, for evaluation and prediction purposes. This understanding requires the identification of process indicators that build the system of variation. To utilize quantitative techniques to understand and improve a software process, more indicators are needed than in a manufacturing process. The need to identify the indicators of a software process and the lack of a generic approach to assess the ability of a software process for quantitative management encouraged us to carry out a sequence of studies that resulted in the development of an Assessment Approach for Quantitative Process Management (A2QPM). This paper explains an application of the A2QPM to the test development process of an avionics software project and presents the results. The study aimed at understanding the effect of the test design stage and the effect of internal reviews as verification activities in test development, with respect to process productivity and product quality measures. The measurement data collected during the execution of the processes were analyzed by control charts to observe the evidence of process stability. The mean values of measurement data were utilized to make performance comparisons between the various executions of the test development process. The results showed that process productivity was unaffected, but the test procedure quality was positively influenced by the application of test design and internal reviews. The utilization of the A2QPM as a guide for the quantitative implementation enabled the systematic evaluation of the test development process and measures prior to analysis. This resulted in the identification of process clusters having stable variation.

Personal Software Process Capability Assessment Method

PDF HTML阅读 XML下载 导出引用 引用提醒 一种个体软件过程能力度量方法 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the National Natural Science Foundation of China under Grant Nos.90718042, 60873072 (国家自然科学基金); the National High-Tech Research and Development Plan of China under Grant Nos.2007AA010303, 2007AA01A127 (国家高技术研究发展计划(863)); the National Basic Research Program of China under Grant No.2007CB310802 (国家重点基础研究发展计划(973)) Personal Software Process Capability Assessment Method Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:个体软件过程(PSP)是由卡内基×梅隆大学软件工程研究所的Humphrey领导开发的.它是一种可用于控制、管理和改进个人工作方式的自我持续改进过程.随着工业界对软件过程改进需求的日益增长,PSP成为了软件组织为达成完全(从宏观到微观)量化过程管理研究中的一个热点课题.软件过程研究表明,高水平的个体软件过程能力是软件项目成功的关键,如何进行有效的个体软件过程能力度量是PSP中的一个核心问题.现有方法不能同时有效处理个体软件过程能力度量中的可变规模收益、多变量输入/输出以及决策者偏好问题.提出了一种综合了数据包络分析(DEA)和层次分析法(AHP)的个体软件过程能力评价方法——PSPADA,介绍了PSPADA的个体软件过程能力评价模型和核心算法(集成决策者偏好和估计规模收益).实验结果显示,PSPADA能够在考虑决策者偏好的同时,有效地进行多指标、规模收益可变的量化评估. Abstract:Personal Software Process (PSP) was introduced by Watts Humphrey in CMU/SEI. It is a measured software process aiming at individual software engineers. With the increasing industrial demand for software process improvement, PSP has become a hot topic for software organizations to achieve the goal of total (from macro to micro) quantitative process management. Since higher process capability is recognized as a determinant of better project performance, it is a critical step to assess the personal software process. However, the assessment of PSP capability exhibits Variable Return to Scale (VRS), Multi-Input-Multi-Output (MIMO) and Decision-Making preference problems, which makes existing traditional assessment methods ineffective. In this paper, a novel Personal Software Process Assessment method by synthesizing Data Envelopment Analysis (DEA) and Analytical Hierarchy Process (AHP)—PSPADA is proposed. PSPADA’s hybrid model and fundamental assessment algorithms (incorporating decision-making preferences and estimating return to scale) are introduced. Experimental results show that the proposed PSPADA model would be particularly helpful in assessing the capability of personal software processes under the MIMO and VRS constraint, by incorporating Decision-Making preferences. 参考文献 相似文献 引证文献

The Case for Quantitative Process Management

This article introduces a special section on Embedding Statistical Methods into Software Engineering Practices. It provides a background on Quantitative Process Management and makes the case for why these methods are important. It presents an example of how a model can be developed to predict project outcomes by using data emerging from the performance of process tasks. It discusses how these methods can be used with different software development paradigms. It ends by summarizing develops needed in five different communities in order for these methods to be widely adopted.

Risk analysis of software process measurements

Quantitative process management (QPM) and causal analysis and resolution (CAR) are requirements of capability maturity model (CMM) levels 4 and 5, respectively. They indicate the necessity of process improvement based on objective evidence obtained from statistical analysis of metrics. However, it is difficult to achieve these requirements in practice, and only a few companies have done so successfully. Evidence-based risk-management methods have been proposed for the control of software processes, but are not fully appreciated, compared to clinical practice in medicine. Furthermore, there is no convincing answer as to why these methods are difficult to incorporate in software processes, despite the fact that they are well established in some business enterprises and industries. In this article, we challenge this issue, point out a problem peculiar to software processes, and develop a generally applicable method for identifying the risk of failure for a project in its early stages. The proposed method is based on statistical analyses of process measurements collected continuously throughout a project by a risk assessment and tracking system (RATS). Although this method may be directly applicable to only a limited number of process types, the fundamental idea might be useful for a broader range of applications.

Testing by the numbers

In order for an organization to reach SEI level 4 capability, quantitative process management is essential. As a step towards SEI level 4 the GSM BSS software organization has developed a metric analysis programme which is being used during the system test phase. This programme focuses on metrics analysis which allows quantitative metric information to be correlated with non-metric knowledge and used real-time. It has allowed us to take a giant leap from simply collecting pass/fail data during testing to analysing the data, so they can be used by the project. Quantitative information is used to plan and monitor test activities and evaluate the product at the end of testing. Not only has this programme provided a technique for managing the test phase, it has provided a mechanism for improving the quality of our releases and we are beginning to use it as a prediction of the quality of our product as release. The metric analysis programme also provides a mechanism for improving the test process and is also being utilized to indicate areas requiring improvement within development. Our paper is a case study of how we use test metrics. It discusses the corrective actions that resulted from the analysis of the metrics. We have found this approach invaluable as we improve the quality of our processes and product.

The capability maturity model: Guidelines for improving the software process

I. THE CAPABILITY MATURITY MODEL FOR SOFTWARE: BACKGROUND, CONCEPTS, STRUCTURES AND USAGE. 1. Introducing Software Process Maturity. The Evolution of the CMM. Immature versus Mature Software Organizations. Fundamental Concepts Underlying Process Maturity. Total Quality Management and the CMM. Customer Satisfaction. Benefits and Risks of Model-Based Improvement. 2. The Software Process Maturity Framework. Behavioral Characterization of the Maturity Levels. Skipping Maturity Levels. Visibility into the Software Process. Prediction of Performance. 3. The Structure of the Capability Maturity Model. Internal Structure of the Maturity Levels . Maturity Levels. Key Process Areas. Key Practices. Common Features. 4. Interpreting the CMM. Interpreting the Key Practices. The Key Process Area Template. Interpreting the Common Features. Organizational Structure and Roles. Understanding Software Process Definition. The Evolution of Processes. Applying Professional Judgment. 5. Using the CMM. A CMM-Based Appraisal Method. Process Assessments and Capability Evaluation. Software Process Improvement. Using the CMM in Context. 6. A High-Maturity Example: Space Shuttle Onboard Software. Introduction. Background. Approaches to Process Improvement. Overall Lessons. II. THE KEY PRACTICES OF THE CAPABILITY MATURITY MODEL FOR SOFTWARE. 7. The Key Areas for Level 2: Repeatable. Requirements Management. Software Project Planning. Software Project Tracking and Oversight. Software Subcontract Management. Software Quality Assurance. Software Configuration Management. 8. The Key Process Areas for Level 3: Defined. Organization Process Focus. Organization Process Definition. Training Program. Integrated Software Management. Software Product Engineering. Intergroup Coordination. Peer Reviews. 9. The Key Process Areas for Level 4: Managed. Quantitative Process Management. Software Quality Management. 10. The Key Process Areas for Level 5:Optimizing. Defect Prevention. Technology Change Management. Process Change Management. Appendix A: References. Appendix B: Acronyms. Appendix C: Glossary. Appendix D: Abridged Version of the Key Practices. Appendix E: Mapping the Key Practices to Goals. Appendix F: Comparing ISO 9001 and the CMM. Appendix G: An Overview of ISO's SPICE Project. Appendix H: Change History of the CMM. Appendix I: Change Request Form. Index. 0201546647T04062001

Quantitative process management in software engineering, a reconciliation between process and product views

The Hughes Aircraft Company, Ground Systems Group, Software Engineering Division, has had a project under way for the past decade to impact significantly the productivity and quality aspects of the software development products that they produce [1]. During 1988 steps were taken to continue reaching for increased improvements over the next 5-year time period. Quantitative process management (QPM) was created to provide management with better views of the underlying instrumented process and a set of techniques for determining the appropriateness of candidate process changes. The accomplishments to date have been in the areas of addressing the apparent duality of the process and product views through the advancement of the concept of a composite metric scoring system. The technology reach is to be able to place a statistical process control harness around the inherently creative software development activities to obtain a more predictable process change manageable system.