在推動能力成熟度整合模式（Capability Maturity Model Integration, CMMI）量化管理等級（ML4）之資訊軟體廠商，須達成組織流程績效（Organizational Process Performance, OPP）與量化專案管理（Quantitative Project Management, QPM）兩個流程領域的規範。其目的在於經由統計工具去分析量化流程績效數據，以建立有效之流程績效模式與流程績效基準，並能及時發現流程績效之變異與瞭解造成流程績效變異的原因，以採取適合的矯正活動，進而滿足流程績效穩定的要求。
製造業使用統計製程管制的管制圖去監控產品與流程品質，文獻中雖有學者提出資訊軟體產業使用管制圖的好處，但卻很少研究該如何應用管制圖來分析軟體流程績效。因此國內已通過CMMI ML3的資訊軟體廠商想利用管制圖分析量化流程績效數據時，因為不知該如何使用與分析結果，造成其邁向CMMI量化管理等級有一定程度上的困難。
本研究把監控製造業生產流程品質的管制圖，與CMMI ML4下13個特定實務（Specific Practices, SP）整合成一組執行流程，並把原本管制圖選擇流程做適度的調整，提出一套針對軟體度量指標特性來選擇適合管制圖的方法。除此之外還建立一系列造成軟體流程績效變異之可能原因，以協助使用此方法之資訊軟體廠商可以初步找出造成軟體流程績效變異的可能原因，並據以制訂矯正的活動。
在個案探討方面，以國內一家已通過CMMI ML3認證的資訊軟體廠商作為研究對象，收集該公司在軟體開發流程中驗收階段的數據，採用本研究所提出的方法來管理組織所制訂的流程績效，以證明本研究所提出方法的可用性。本研究結果可以作為目前國內已通過ML3欲通過ML4之廠商在採用統計製程管制圖來管理組織流程績效的參考。

For organizations who want to enhance their maturity level to CMMI ML4, their processes need to satisfy the requirements of Organizational Process Performance (OPP) and Quantitative Project Management (QPM) process areas. One of practices for these organizations is the use of statistical tools to analyze quantitative process performance data and further establish effective process performance models (PPM) and process performance baselines (PPB), find the variation of process performance and understand the causes of variation and further adopt the suitable corrective actions, and hence satisfy the requirements of process performance stability.
The manufacturing industry has adopted statistical process control charts to monitor the quality of processes and products. Although researchers in the literature have proposed to use control chart in software industry, few studies have conducted on how to use the control chart to manage the software process performance. As a result, the CMMI ML3 software organizations have difficulties to use control charts to analyze their quantitative process performance data. It also results in a certain degree of difficulties for domestic CMMI ML3 organizations to enhance their maturity level to ML4.
This study integrates statistical process control charts with 13 specific practices of ML4 OPP and QPM into a set of processes, proposes a selection model of statistical process control charts for software industry based on the characteristics of the adopted software process metrics. In addition, this study also establishes a series of causes which may cause the variation of software processes performance to help software vendors early identify the possible causes and thus make corrective actions to assist organizations in stabilizing their software process performance.
The study also conducts a case study to verify the feasibility of the proposed methods in this study. This study collected process performance data at the acceptance phase from a domestic CMMI ML 3 company, and then adopted the proposed methods to manage the process performance. The proposed method in this study can help those CMMI ML3 organizations use statistical process control charts to quantitatively manage their process performances and further achieve their process maturities to CMMI ML4.

中文部分
[1]中華民國資訊軟體協會. 經濟部工業局提升資訊軟體品質(CMMI)計畫網站. Available from: http://www.cmmi-taiwan.org.tw/.
[2]凌羣, 專案導入結案報告書. 2005.
[3]楊素芬, 品質管理. 2006, 台北市: 華泰.
[4]經濟部工業局, 台灣CMMI高階能力成熟度整合模式研討會總結報告. 2007.
[5]鄭春生, 品質管理（修訂版）. 1999, 台北市: 育友.

英文部分
[6]Baldassarre, T., et al., Managing Software Process Improvement (SPI) through Statistical Process Control (SPC). Lecture notes in computer science, 2004. 3009: p. 30-46.
[7]Boffoli, N., et al. Statistical process control for software: A systematic approach. in ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement. 2008.
[8]Caivano, D. Continuous software process improvement through statistical process control. 2005.
[9]Cangussu, J., R. DeCarlo, and A. Mathur, Monitoring the software test process using statistical process control: A logarithmic approach. ACM SIGSOFT Software Engineering Notes, 2003. 28(5): p. 158-167.
[10]Card, D., Statistical process control for software? IEEE Software, 1994. 11(3): p. 95-97.
[11]Card, D.N. and R.A. Berg, An industrial engineering approach to software development. The Journal of Systems and Software, 1989. 10(3): p. 159-168.
[12]Choi, K. and D.H. Bae, Dynamic project performance estimation by combining static estimation models with system dynamics. Information and Software Technology, 2009. 51(1): p. 162-172.
[13]Chulani, S. and B. Boehm, Modeling software defect introduction and removal: COQUALMO (COnstructive QUALity MOdel). USC-CSE Technical Report, 1999: p. 99-510.
[14]Dumke, R., et al., Software Process Measurement and Control a Measurement Based Point of View of Software Processes. 2006: Univ., Fak. fur Informatik.
[15]Dumke, R., I. Cote, and O. Andruschak, Statistical Process Control (SPC): A Metrics Based Point of View of Software Processes Achieving the CMMI Level Four. 2004: Univ., Fak. fur Informatik.
[16]Ebenau, R., Predictive quality control with software inspections. Crosstalk, 1994. 7(6): p. 9-16.
[17]Eickelmann, N. and A. Anant, Statistical process control: what you don't measure can hurt you! IEEE Software, 2003. 20(2): p. 49-51.
[18]Florac, W., A. Carleton, and J. Barnard, Statistical process control: analyzing space shuttle onboard software process. IEEE Software, 2000. 17(4): p. 97-106.
[19]Florence, A., CMM Level 4 Quantitative Analysis and Defect Prevention. Crosstalk, Feb, 2001.
[20]Hollenbach, C. and D. Smith, A portrait of a CMMISM Level 4 effort. Systems Engineering, 2002. 5(1): p. 52-61.
[21]Jacob, A. and S. Pillai, Statistical process control to improve coding and code review. IEEE Software, 2003. 20(3): p. 50-55.
[22]Jacobs, J., et al., Identification of factors that influence defect injection and detection in development of software intensive products. Information and Software Technology, 2007. 49(7): p. 774-789.
[23]Jalote, P. and A. Saxena, Optimum control limits for employing statistical process control in software process. IEEE Transactions on Software Engineering, 2002. 28(12): p. 1126-1134.
[24]Kan, S., Metrics and models in software quality engineering. 2002: Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA.
[25]Komuro, M. Experiences of applying SPC techniques to software development processes. 2006: ACM New York, NY, USA.
[26]Kulpa, M. and K. Johnson, Interpreting the Cmmi: A Process Improvement Approach. 2003: CRC Press.
[27]Lantzy, M. Application of statistical process control to the software process. 1992: ACM New York, NY, USA.
[28]Leszak, M., D.E. Perry, and D. Stoll, Classification and evaluation of defects in a project retrospective. Journal of Systems and Software, 2002. 61(3): p. 173-187.
[29]Paulk, M. Applying SPC to the personal software process. 2000.
[30]Sargut, K. and O. Demirors, Utilization of statistical process control (SPC) in emergent software organizations: pitfalls and suggestions. Software Quality Journal, 2006. 14(2): p. 135-157.
[31]Song, Q., et al., Software defect association mining and defect correction effort prediction. IEEE Transactions on Software Engineering, 2006. 32(2): p. 69-82.
[32]Tarhan, A. and O. Demirors, Investigating Suitability of Software Process and Metrics for Statistical Process Control. Lecture notes in computer science, 2006. 4257: p. 87.
[33]Team, C.P., CMMI for Development (Version 1.2). Software Engineering Institute of Carnegie Mellon University, 2006.
[34]Weller, E., B. Syst, and A. Phoenix, Practical applications of statistical process control [in softwaredevelopment projects]. IEEE Software, 2000. 17(3): p. 48-55.