The Organisational Factors of Software Process Improvement in Small Software Industry: Comparative Study

Basri, Shuib; Almomani, Malek Ahmad; Imam, Abdullahi Abubakar; Thangiah, Murugan; Gilal, Abdul Rehman; Balogun, Abdullateef Oluwagbemiga

doi:10.1007/978-3-030-33582-3_106

Cited by 15 publications

(9 citation statements)

References 26 publications

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“…Software development lifecycle (SDLC) is a defined process specifically created for the development of reliable and high-quality software systems. The stages embedded in SDLC such as requirement gathering, requirement analysis, system design, system development and maintenance are stepwise and must be strictly observed to ensure a timely and efficient software system [1][2][3]. Nonetheless, human errors or mistakes are inevitable even though most of the stages in SDLC are conducted by professionals.…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction

et al. 2021

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Selecting the most suitable filter method that will produce a subset of features with the best performance remains an open problem that is known as filter rank selection problem. A viable solution to this problem is to independently apply a mixture of filter methods and evaluate the results. This study proposes novel rank aggregation-based multi-filter feature selection (FS) methods to address high dimensionality and filter rank selection problem in software defect prediction (SDP). The proposed methods combine rank lists generated by individual filter methods using rank aggregation mechanisms into a single aggregated rank list. The proposed methods aim to resolve the filter selection problem by using multiple filter methods of diverse computational characteristics to produce a dis-joint and complete feature rank list superior to individual filter rank methods. The effectiveness of the proposed method was evaluated with Decision Tree (DT) and Naïve Bayes (NB) models on defect datasets from NASA repository. From the experimental results, the proposed methods had a superior impact (positive) on prediction performances of NB and DT models than other experimented FS methods. This makes the combination of filter rank methods a viable solution to filter rank selection problem and enhancement of prediction models in SDP.

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Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction

et al. 2021

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“…The rapid and continuous influence of software systems on human activities cannot be over-emphasized. This influence can be attributed to the comfort and pleasure derived from using these software systems [ 1 , 2 ]. Developing quality and reliable software systems become imperative as the adverse effect of defective software systems may be disastrous.…”

Section: Introductionmentioning

confidence: 99%

SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction

Balogun

Lafenwa-Balogun

Mojeed

et al. 2020

Lecture Notes in Computer Science

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Class imbalance is a prevalent problem in machine learning which affects the prediction performance of classification algorithms. Software Defect Prediction (SDP) is no exception to this latent problem. Solutions such as data sampling and ensemble methods have been proposed to address the class imbalance problem in SDP. This study proposes a combination of Synthetic Minority Oversampling Technique (SMOTE) and homogeneous ensemble (Bagging and Boosting) methods for predicting software defects. The proposed approach was implemented using Decision Tree (DT) and Bayesian Network (BN) as base classifiers on defects datasets acquired from NASA software corpus. The experimental results showed that the proposed approach outperformed other experimental methods. High accuracy of 86.8% and area under operating receiver characteristics curve value of 0.93% achieved by the proposed technique affirmed its ability to differentiate between the defective and non-defective labels without bias.

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Section: Introductionmentioning

confidence: 99%

“…Software Process Improvement (SPI) models are developed to improve the development process within companies and it mainly oriented towards large companies [17]. The SPI models are very difficult to be adopted in the Small and Medium software Enterprises (SMEs) industry because it requires large software process details [17,18]. In the Malaysian context, several studies indicated that Malaysian SME software development companies face myriad challenges to implement SPI efforts [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Using an Expert Panel to Validate the Malaysian SMEs-Software Process Improvement Model (MSME-SPI)

Almomani

Basri

Almomani

et al. 2020

Software Engineering Perspectives in Intelligent Systems

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This paper presents the components of a newly developed Malaysian SMEs -Software Process Improvement model (MSME-SPI) that can assess SMEs software development industry in managing and improving their software processes capability. The MSME-SPI is developed in response to practitioner needs that were highlighted in an empirical study with the Malaysian SME software development industry. After the model development, there is a need for independent feedback to show that the model meets its objectives. Consequently, the validation phase is performed by involving a group of software process improvement experts in examining the MSME-SPI model components. Besides, the effectiveness of the MSME-SPI model is validated using an expert panel. Three criteria were used to evaluate the effectiveness of the model namely: usefulness, verifiability, and structure. The results show the model effective to be used by SMEs with minor modifications. The validation phase contributes towards a better understanding and use of the MSME-SPI model by the practitioners in the field.

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The Organisational Factors of Software Process Improvement in Small Software Industry: Comparative Study

Cited by 15 publications

References 26 publications

Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction

Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction

SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction

Using an Expert Panel to Validate the Malaysian SMEs-Software Process Improvement Model (MSME-SPI)

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