Application of 80/20 rule in software engineering Waterfall Model

Iqbal, Muhammad Zohaib; Rizwan, Muhammad

doi:10.1109/icict.2009.5267186

Cited by 26 publications

(14 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Table 4, the frequency of intensification steps and diversification steps in CCLS is approximately 80 and 20 percent, which is a desirable proportion for SLS algorithms, as suggested in the literature [30]. Moreover, the famous 80/20 rule has been applied in many fields, such as collection management [31], library loans [32] and software engineering [33]. Our experiments also justify this implication about the 80/20 rule in SLS algorithms.…”

Section: Differences Between the Ccm Heuristic And Previous CC Based supporting

confidence: 71%

CCLS: An Efficient Local Search Algorithm for Weighted Maximum Satisfiability

Luo

Zhong

et al. 2015

IEEE Trans. Comput.

View full text Add to dashboard Cite

The maximum satisfiability (MAX-SAT) problem, especially the weighted version, has extensive applications. Weighted MAX-SAT instances encoded from real-world applications may be very large, which calls for efficient approximate methods, mainly stochastic local search (SLS) ones. However, few works exist on SLS algorithms for weighted MAX-SAT. In this paper, we propose a new heuristic called CCM for weighted MAX-SAT. The CCM heuristic prefers to select a CCMP variable. By combining CCM with random walk, we design a simple SLS algorithm dubbed CCLS for weighted MAX-SAT. The CCLS algorithm is evaluated against a state-of-the-art SLS solver IRoTS and two state-of-the-art complete solvers namely akmaxsat_ls and New WPM2, on a broad range of weighted MAX-SAT instances. Experimental results illustrate that the quality of solution found by CCLS is much better than that found by IRoTS, akmaxsat_ls and New WPM2 on most industrial, crafted and random instances, indicating the efficiency and the robustness of the CCLS algorithm. Furthermore, CCLS is evaluated in the weighted and unweighted MAX-SAT tracks of incomplete solvers in the Eighth Max-SAT Evaluation (Max-SAT 2013), and wins four tracks in this evaluation, illustrating that the performance of CCLS exceeds the current state-of-the-art performance of SLS algorithms on solving MAX-SAT instances.

show abstract

Section: Differences Between the Ccm Heuristic And Previous CC Based supporting

confidence: 71%

CCLS: An Efficient Local Search Algorithm for Weighted Maximum Satisfiability

Luo

Zhong

et al. 2015

IEEE Trans. Comput.

View full text Add to dashboard Cite

show abstract

“…The reason for 80% comes from the Pareto chart. A cumulative percentage line in a pareto chart helps DMs to judge the added contribution of each category (Craft and Leake 2002;Iqbal and Rizwan 2009). According to Figure 4, the DMs should focus on improvements to the first four highest RPN scores because 78.5% (nearly 80%) of the multimodal green logistics risk occurs from these risk factors.…”

Section: )mentioning

confidence: 99%

The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study

Kengpol

Tuammee

2015

International Journal of Production Research

View full text Add to dashboard Cite

The objective of this research was to develop a decision support framework (DSF) to assess quantitative risk in multimodal green logistics. This risk assessment is the combination of a number of models, the failure mode and effects analysis, the risk contour plot, the quantitative risk assessment, the analytic hierarchy process and the data envelopment analysis which can support a user to perform risk assessment in various decisions. The contribution of this research is that the risk assessment model can generate an optimal green logistics route in accordance with weight from the user. The highlight of this DSF is that the quantitative assessment model can reduce bias on risk assessment of logistics route. An in-depth case study, recommendations, limitations and further research are also provided.

show abstract

“…Today's developers operate under SDLC frameworks including agile [167], waterfall [168], and spiral [169], as well as other models. A significant number of developers operate under less explicit frameworks organized around GitHub practices-and this practice dominates in components used in many a Big Data stack.…”

Section: Big Data Sdlc Standards and Guidelinesmentioning

confidence: 99%

NIST Big Data Interoperability Framework: volume 4, security and privacy, version 2

2018

View full text Add to dashboard Cite

NIST Special Publication series 1500 is intended to capture external perspectives related to NIST standards, measurement, and testing-related efforts. These external perspectives can come from industry, academia, government, and others. These reports are intended to document external perspectives and do not represent official NIST positions. Certain commercial entities, equipment, or materials may be identified in this document to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose. There may be references in this publication to other publications currently under development by NIST in accordance with its assigned statutory responsibilities. The information in this publication, including concepts and methodologies, may be used by federal agencies even before the completion of such companion publications. Thus, until each publication is completed, current requirements, guidelines, and procedures, where they exist, remain operative. For planning and transition purposes, federal agencies may wish to closely follow the development of these new publications by NIST. Organizations are encouraged to review all draft publications during public comment periods and provide feedback to NIST. All NIST publications are available at http://www.nist.gov/publication-portal.cfm.

show abstract

Application of 80/20 rule in software engineering Waterfall Model

Cited by 26 publications

References 1 publication

CCLS: An Efficient Local Search Algorithm for Weighted Maximum Satisfiability

CCLS: An Efficient Local Search Algorithm for Weighted Maximum Satisfiability

The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study

NIST Big Data Interoperability Framework: volume 4, security and privacy, version 2

Contact Info

Product

Resources

About