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CHAOS Report Success Factors by Rank

CHAOS Report Success Factors by Rank

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Article
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Sixteen years after the publication of the Standish Group's first CHAOS report in 1994, there is little cause for celebration. True, system development project (SDP) success rates have improved to 32% from the benchmark low of 16.2%; however, when 68% of projects are either cancelled or seriously challenged with regard to budget, schedule, or proje...

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Context 1
... CHAOS 1994, the Standish Group also identified a top ten list of critical factors responsible for project success (Table 1). Many of these factors are the semantic inversion of the failure factors. ...
Context 2
... for some minor rewording, the top three CSFs have remained the same since the original 1994 report. Table 1 summarizes CHAOS critical success factors (CSFs) by survey year for information available through publicly available sources, as the CHAOS Report is proprietary. ...

Citations

... Another important concern that affects prioritization is negligible communication with stakeholders while prioritization (Tonella et al., 2013). Literature suggests most prioritization techniques do not support communication among stakeholders and has led to project failures in past (Attarzadeh and Ow, 2008;Gold et al., 2010;Savolainen, 2011;The Standish Group, 2009). The work carried out in Perini et al. (2013) has reported this as one of the limitations of their requirement prioritization technique. ...
Article
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The success of requirement prioritization process largely depends upon how well different constraints and influential factors are handled by stakeholders and developers while prioritization. The main goal of this research is to present a semi-automated dependency based collaborative requirement prioritization approach (CDBR), which uses linguistic values, execute-before-after (EBA) relation among requirements and machine learning algorithm to minimize the difference of opinion between stakeholder and developers for effective collaboration and for better approximation of final prioritization results, acceptable to both. The presented approach targets three major constraints rarely addressed in existing work, namely dependencies among requirements, communication among stakeholder and developers and the issue of scalability. Results of performance assessment conducted on several different requirement sets and on a case study by comparing CDBR with other state of the art approaches namely, AHP and IGA. The results are accurate and comparable in terms of effectiveness, efficiency, scalability and disagreement concerns among stakeholder and developers which in turn provides robustness to decision making process of awarding more importance to some requirements over others. CDBR overpowers AHP and IGA in terms of efficiency and processing time respectively.
... In Table 2 Other 10 10 Table 2: CHAOS report success factors by rank It also published the results of its first survey concerning the performance of IT projects, using a combination of surveys through questionnaires, interviews from senior executives of organizations and focus groups [5]. Table 3 that follows presents these findings which are divided into two categories. ...
... In the left column there are the ten key factors that lead a project to be characterized as challenged and in the right the 10 key features that lead a project to its abandonment. (1995), The CHAOS report [9] Although the Chaos Report is one of the most common references regarding the statistical analysis of IT projects (successful, failed, challenged), in the last years some scholars have questioned the survey methodology of Standish Group [5]. ...
... According to him, the IT projects exhibit a failure rate ranging between 10% and 15% [4]. But the data that he provides to support his views is unofficial [5]. ...
Conference Paper
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Recent research findings indicate that the proportion of the successful IT projects, over time, increases. However, it is often observed that the majority of IT projects fail, or their success is disputable, something that entails cost and time inefficiencies. This research studies the IT project failures in the hotel industry in Greece, in order to present not only the proportion of the failed projects, but also the factors that determine their outcome. The research was undertaken in the last quarter of 2015. It shows that four out of ten IT projects meet the initially defined objectives, while the others are questioned or, in some cases, abandoned, a finding that is consistent with other research findings concerning IT projects in general. Furthermore, in order for an IT project to enhance its success possibility, executive management support, user involvement and a clear statement of requirements and objectives are critical prerequisites. This study aims to provide the organization with a framework that highlights the key success factors.
... These factors need to be addressed and thereafter they need to be controlled. Consequently , we presented 'top-ten' based on survey Boehm's 10 risk items 1991 on software risk management [24] , the top 10 risk items according to a survey of experienced project managers , Boehm's 10 risk items 2002 and Boehm's 10 risk items 2006-2007, Miler [25] , The Standish Group survey [26], Addison and Vallabh [27], Addison [28], Khanfar, Elzamly et al. [10], Elzamly and Hussin [11], Elzamly and Hussin [9], Aloini et al. [29], Han and Huang [30] [31], Aritua et al. [32], Schmidt et al. [33], Mark Keil et al. [34], Nakatsu and Iacovou [35], Chen and Huang [36], Mark Keil et al. [37], Wallace et al. [38], Sumner [39], Boehem and Ross [40], Ewusi-Mensah [41], Pare et al. [42], Houston et al. [43], Lawrence et al. [44] , Shafer and Offi- cer [45], hoodat and Rashidi [23], Jones et al. [46], Jones [47], Taimour [48] , and other scholars , researchers in software engineering, to obtain software risk factors and risk management techniques. These software project risks are illustrated inTable 1. ...
Article
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Risk is not always avoidable, but it is controllable. The aim of this paper is to present new techniques which use the stepwise regression analysis to model and evaluate the risks in planning software development and reducing risk with software process improvement. Top ten software risk factors in planning software development phase and thirty control factors were presented to respondents. This study incorporates risk management approach and planning software development to mitigate software project failure. Performed techniques used stepwise regression analysis models to compare the controls to each of the risk planning software development factors, in order to determine and evaluate if they are effective in mitigating the occurrence of each risk planning factor and, finally, to select the optimal model. Also, top ten risk planning software development factors were mitigated by using control factors. The study has been conducted on a group of software project managers. Successful project risk management will greatly improve the probability of project success.
... Consequently, we presented 'top-ten' based on survey Boehm's 10 risk items 1991 on software risk management (Boehm, 1991), the top 10 risk items according to a survey of experienced project managers, Boehm's 10 risk items 2002 and Boehm's 10 risk items 2006-2007, (Miler, 2005, The Standish Group survey (CHAOS, 1995), (Addison & Vallabh, 2002), (Addison, 2003), Khanfar, Elzamly et al. (Khanfar et al., 2008), (Elzamly & Hussin, 2011), (Ezamly & Hussin, 2011), (Aloini, Dulmin, & Mininno, 2007), (Han & Huang, 2007)- (Huang & Han, 2008), (Aritua, Smith, & Bower, 2010), (Schmidt, Lyytinen, Keil, & Cule, 2001), (Keil, Cule, Lyytinen, & Schmidt, 1998), (Nakatsu & Iacovou, 2009), (J.-C. Chen & Huang, 2009), (Keil, Tiwana, & Bush, 2002), (Wallace, Keil, & Rai, 2004), (Sumner, 2000), (Boehm & Ross, 1989), (Kweku Ewusi-Mensah, 2003), (Paré, Sicotte, Jaana, & Girouard, 2008), (Houston, Mackulak, & Collofello, 2001), (Lawrence, Wiegers, & Ebert, 2001), (Shafer & Officer, 2004), (Hoodat & Rashidi, 2009), (Christopher Jones, Glen, Anna, & Miller, 2010, (Capers Jones, 2008), (Taimour, 2005), and another scholars, researchers in software engineering to obtain software risk factors and risk management techniques, these software risks are: Failure to utilize a phased delivery approach 2 4 ...
Article
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This paper aims to present new techniques to determine if fuzzy and stepwise regression are effective in mitigating the occurrence software risk factor in the implementation phase. The proposed process compares the accuracy of prediction betweenstepwise multiple regression analysis techniques and fuzzy multiple regression. In addition, After applying MRE, the results show that the most value of MMRE in fuzzy multiple regression modelling for risks were slightly higher than the value of MMRE in stepwise multiple regression except risk 1 models around about fuzzy regression. Therefore, the most value of Pred (25) fuzzy multiple regression model for risks were slightly higher than or equal the value of pride (25) stepwise multiple regression except 9were slightly higher than stepwise. The model’s accuracy slightly improves in fuzzy multiple regression than stepwise multiple regression. Successful application of software project risk management will greatly improve the probability of project success.
... Jones et. al. [7] analyzed Chaos report success factors by ranking them from 1994 to 2008. Eveleens and Verhoef in [15] claim that Chairman of Standish Group has replied that " All data and information in the Chaos reports and all Standish reports should be considered as Standish opinion " . ...
Article
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Systematic software development process involves estimation of size, effort, schedule and cost of a software project and analysis of critical factors affecting these estimates. In literature there are many methods for software estimation and categorization of critical factors. More than 50% of the projects undertaken have challenged the initially proposed estimates. Even if we consider updating estimates at various phases of software development, the percentage of challenged projects reduces marginally. The reason for such a situation is that the decisions are made on historical and collected data. Therefore, software data collection to a reasonable accuracy and its validation is important both for decision making and validating software development process. In this paper an effort is made to highlight the importance of software data collection. Collected data is utilized to validate effort estimation model formulated by the authors. Comparison of effort values obtained from popular estimation models is also made. The data collected has also helped in identifying the critical factors affecting the estimates.
Conference Paper
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With the recent advancements of technology, Ubiquitous Systems have rapidly become popular all over the world. It is a new paradigm that focuses on smooth integration of technology in human environments enabling users to access information and functionality anytime and anywhere. Software development companies nowadays increasingly invest in the ubiquitous system development projects in order to stay competitive and survive in the IT Industry. Success of ubiquitous system development projects heavily depends on Nonfunctional user requirements. Identification of the nonfunctional requirements is challenging since it represents the quality attributes of the system and are not directly measurable. This quantitative research aims to evaluate the different types of non-functional requirements that significantly contribute to the success of ubiquitous system development projects. This study was based on the data collected from the software industry in Sri Lanka. The results of this study indicate that both the product-related and organizational-related non-functional requirements strongly affect the ubiquitous systems success. The findings provide insights to the vendors of ubiquitous system development companies in the software industry.
Article
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Information systems are the basis used as support for the company’s business strategy. In the application of information systems, information system risk management needs to be done. In carrying out information system risk management, assessment is needed. The IT department of Rajawali Citra Televisi Indonesia company has a target of completing 600 tickets every month. The number of tickets that exceed the target of the IT department, has a gap to be a factor that influences the risk of information systems. The research was conducted to find information system risk factors and build an information system risk management assessment model. The method used in this research is confirmatory factor analysis. The factor influencing information risk is the threat factor to infrastructure. The formed model that can be used for risk management information system assessment is P = 6,934 + 1,184 X4. System risk can be said to be ideal if the company can reduce the threat factor to infrastructure, so that the risk of information systems can be minimized.
Article
This article presents a systematic approach to prioritize requirements and estimate risk associated with each requirement. It first aims at providing short training to both developers and stakeholders to bridge the gap of understanding and comprehend requirements so that a refined priority value for each requirement can be obtained. Secondly, it presents a requirement risk and re-prioritization estimation model to make sure that a right decision has been taken by stakeholder and developers. The entire process has been supported with an example case study and by a survey that is conducted at IT companies. The response for the applicability of the proposed approach from the industry is appreciable and is promising as it will help in minimizing the disagreements between stakeholders and developers, thereby resulting in better collaboration. It will also help in minimizing the overall risk associated with development as only the most important functionality will be delivered in right sequence to the client thereby reducing the overall development time.
Article
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Software projects require a right mix of the software resources and the expertise to increase the chances of timely completion. The interface for the resource allocation to the software projects is provided by the project factors. The identification of the comprehensive project factors for the diversified nature of projects in itself is an open research area. This paper is based on a quantitative study that helps in identifying the prominent software project factors for large scale projects. The paper then, as a result provides a list of project success factors and provides the statistical evidence to support the result of the survey.