Estimating software project effort for manufacturing firms

Chou, Jui‐Sheng; Wu, Cheng-Chieh

doi:10.1016/j.compind.2013.04.002

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…A feasible estimation model is presented (Chou and Wu, 2013) to estimate satisfactorily the person-hour of ERP system development project based on analysis of recent ERP software projects. Modeling the effort usage proportion in different phases of software development and systematic data analysis framework, offering better visualization and interpretations of the effort distribution in specific phases is presented (Chatzipetrou et al , 2015).…”

Section: Resultsmentioning

confidence: 99%

Quality and reliability studies in software defect management: a literature review

Pai¹,

Joshi

Rane

2021

IJQRM

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PurposeThis paper is focused at studying the current state of research involving the four dimensions of defect management strategy, i.e. software defect analysis, software quality, software reliability and software development cost/effort.Design/methodology/approachThe methodology developed by Kitchenham (2007) is followed in planning, conducting and reporting of the systematic review. Out of 625 research papers, nearly 100 primary studies related to our research domain are considered. The study attempted to find the various techniques, metrics, data sets and performance validation measures used by researchers.FindingsThe study revealed the need for integrating the four dimensions of defect management and studying its effect on software performance. This integrated approach can lead to optimal use of resources in software development process.Research limitations/implicationsThere are many dimensions in defect management studies. The authors have considered only vital few based on the practical experiences of software engineers. Most of the research work cited in this review used public data repositories to validate their methodology and there is a need to apply these research methods on real datasets from industry to realize the actual potential of these techniques.Originality/valueThe authors believe that this paper provides a comprehensive insight into the various aspects of state-of-the-art research in software defect management. The authors feel that this is the only research article that delves into the four facets namely software defect analysis, software quality, software reliability and software development cost/effort.

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Section: Resultsmentioning

confidence: 99%

Quality and reliability studies in software defect management: a literature review

Pai¹,

Joshi

Rane

2021

IJQRM

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“…Data mining sudah diterapkan dalam banyak bidang antara lain: estimasi proyek perangkat lunak untuk perusahaan manufaktur (Chou & Wu, 2013), prediksi terjadinya gempa bumi (Moustra, Avraamides, & Christodoulou, 2011), klasifikasi web spam (Fdez-Glez et al, 2015), deteksi klastering sel kanker pada bagian payudara (Oliver et al, 2012) dan penyusunan tata letak barang pada supermarket melalui asosociation rule berdasarkan kebiasaan pembelian konsumen (Cil, 2012). Secara umum kegunaan dari data mining adalah estimasi, prediksi, klasifikasi, klastering dan asosiasi.…”

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Average Weight Information Gain Untuk Menangani Data Berdimensi Tinggi Menggunakan Algoritma C4.5

Suntoro¹,

Indah²

2017

JBI

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Abstract. In the recent decades, a large data are stored by companies and organizations. In terms of use, big data will be useless if not processed into information according to the usability. The method used to process data into information is called data mining. The problem in data mining especially classification is data with a number of attributes that many and each attribute are irrelevant. This study proposes attribute weighting method using weight information gain method, then the attribute weights calculates the average value. Having calculated the average value of the attribute selection, the selected attributes are those with a value weights above average value. Attributes are selected then performed using an algorithm C4.5 classification, this method is named Average Weight Information Gain C4.5 (AWEIG-C4.5). The results show that AWEIG-C4.5 method is better than C4.5 method with the accuracy of the average value of each is 0.906 and 0.898.Keywords: data mining, high dimensional data, weight information gain, C4.5 algorithm Abstrak. Dalam beberapa dekade terakhir, data yang besar disimpan oleh perusahaan dan organisasi. Dari segi penggunaan, data besar tersebut akan menjadi tidak berguna jika tidak diolah menjadi informasi yang sesuai dengan kegunaan. Metode yang digunakan untuk mengolah data menjadi informasi adalah data mining. Masalah dalam data mining khususnya klasifikasi adalah data dengan jumlah atribut yang banyak atau dalam bahasa komputer disebut data berdimensi tinggi. Pada penelitian ini diusulkan metode pembobotan atribut menggunakan metode weight information gain, kemudian bobot atribut tersebut dihitung nilai rata-rata. Setelah dihitung nilai rata-rata dilakukan pemilihan atribut, atribut yang dipilih adalah atribut dengan nilai bobot di atas nilai rata-rata. Atribut yang terpilih kemudian dilakukan klasifikasi menggunakan algoritma C4.5, metode ini diberi nama Average Weight Information Gain C4.5 (AWEIG-C4.5). Hasil penelitian menunjukkan metode AWEIG-C4.5 lebih baik daripada metode C4.5 dengan nilai rata-rata akurasi masing-masing adalah 0,906 dan 0,898. Dari uji paired t-Test terdapat perbedaan signifikan antara metode AWEIG C4.5 dengan metode C4.5.Kata Kunci: data mining, data berdimensi tinggi, weight information gain, algoritma C4.5

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“…21,500) of them were late, over budget, and/or partially meeting stakeholders' needs; and 18% (i.e. 9,000) of projects totally failed (cancelled prior to completion or delivered and never used) [3].…”

Section: Introductionmentioning

confidence: 99%

Clustering and Artificial Neural Network Ensembles Based Effort Estimation

Moustapha

Far

2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Accurate effort estimation of software development projects plays a key role in project success. However, it is still a challenge activity to researchers and practitioners because of the nature of software products and dynamics in software industry and development environment. Artificial neural network (ANN) is as an effective method and has been widely used in various areas of software engineering. This paper proposes a new effort estimation method based on clustering and ANN ensembles. The contribution of the paper is twofold. First, the impact of clustering projects on the estimation accuracy is investigated. Second, the impact of using ANN ensembles instead of a single ANN is also investigated. The proposed method includes three phases called pre-processing, k-means clustering, and ANN ensembles effort estimation. The method starts with exploring the historical projects dataset. Afterward, k-means is used to cluster the projects. Finally, the proposed method as well as two other estimation methods (i.e. a single ANN and expert-based) were applied to the created clusters and results were compared using MMRE and PRED measures. The simulation results show that the proposed method significantly outperforms the two other estimation methods.

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Estimating software project effort for manufacturing firms

Cited by 7 publications

References 12 publications

Quality and reliability studies in software defect management: a literature review

Quality and reliability studies in software defect management: a literature review

Average Weight Information Gain Untuk Menangani Data Berdimensi Tinggi Menggunakan Algoritma C4.5

Clustering and Artificial Neural Network Ensembles Based Effort Estimation

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