Finding an effective classification technique to develop a software team composition model

Gilal, Abdul Rehman; Jaafar, Jafreezal; Capretz, Luiz Fernando; Omar, Mazni; Basri, Shuib; Aziz, Izzatdin Abdul

doi:10.1002/smr.1920

Cited by 24 publications

(15 citation statements)

References 69 publications

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“…These personality traits can be assessed using established assessment tools that are available in the market. Amongst the famous ones in computing and psychology domains are as Myer-Briggs Type Indicator (MBTI), Keirsey Temperament Sorter (KTS), Five-Factor Model (FFM) or famously known as The Big Five [20]) and HEXACO [21]. For this study, the personality attributes were extracted from HEXACO personality inventory items which comprise six traits namely Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness and Openness to Experience.…”

Section: Team Formation/compositionmentioning

confidence: 99%

“…However, just combining team members' personalities is still not enough to form an ideal team such that it concerns only the "soft" skills portion. The good combination should also include "hard" skills [20]. Hard skills refer to technical skill and soft skills refer to non-technical such as personalities, interpersonal and intrapersonal skills.…”

Section: Team Formation/compositionmentioning

confidence: 99%

See 1 more Smart Citation

Team Formation for Agile Software Development: A Review

Zainal¹,

Razali²,

Mansor³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Rapid and unpredictable technology advancements cause a rise in demand for software products that can be delivered faster, adapt to volatile changes and save cost. Over the years, Agile Software Development (ASD) becomes more suitable as the software process that can cater to those demands while still maintaining the quality desired. With this Agile nature, it is apparent that the team that develops the software product need to have unique features as well. The team should also be formed correctly to gain effectiveness, strong performance and ultimately project success. Ineffective teams can be caused by, among others, conflict, inadequate skillset, unbalanced role assignment, lack of teamwork and non-competent team members. However, by far, the characteristics that need to be considered in forming effective teams are yet to be formalized. Thus, this study aims to identify the necessary characteristics in composing a competent team that is well balanced and can create coherent teamwork. The study involves a qualitative literature review which includes past studies about team formation specifically in the software development domain. The data were collected from online journal databases and analyzed using content analysis. From the analysis, six characteristics together with their attributes were identified as vital in team formation. Another two characteristics were also identified as influencing the sixteam formation characteristics. These findings will need further empirical rigor before they can become a complete Agile Software Development team composition model. This model is believed to assist Agile practitioners in forming effective teams for their development projects.

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Section: Team Formation/compositionmentioning

confidence: 99%

Section: Team Formation/compositionmentioning

confidence: 99%

Team Formation for Agile Software Development: A Review

Zainal¹,

Razali²,

Mansor³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Simulated Annealing is a probabilistic technique for global optimum approximation of a function, especially in a large search space. It is inspired by annealing in metallurgy, a technique that involves heating and cooling of a material for size increase and reduction of defects [3], [9]. PSO, on the other hand, is a computational method that solves optimization problem by iterative improvement of the candidate solution using cognitive and social terms as measures of quality [10].…”

Section: Metaheuristics: Annealing Algorithm and Particle Swarm Omentioning

confidence: 99%

Towards an Improved Strategy for Solving Multi-Armed Bandit Problem

Akanmu*¹,

Garg²,

Gilal³

2019

IJITEE

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Multi-Armed Bandit (MAB) problem is one of the classical reinforcements learning problems that describe the friction between the agent’s exploration and exploitation. This study explores metaheuristics as optimization strategies to support Epsilon greedy in achieving an improved reward maximization strategy in MAB. In view of this, Annealing Epsilon greedy is adapted and PSO Epsilon greedy strategy is newly introduced. These two metaheuristics-based MAB strategies are implemented with input parameters, such as number of slot machines, number of iterations, and epsilon values, to investigate the maximized rewards under different conditions. This study found that rewards maximized increase as the number of iterations increase, except in PSO Epsilon Greedy where there is a non-linear behavior. Our AnnealingEpsilon greedy strategy performed better than Epsilon Greedy when the number of slot machines is 10, but Epsilon greedy did better when the number of slot machines is 5. At the optimal value of Epsilon, which we found at 0.06, Annealing Epsilon greedy performed better than Epsilon greedy when the number of iterations is 1000. But at number of iterations ≥ 1000, Epsilon greedy performed better than Annealing Epsilon greedy. A stable reward maximization values are observed for Epsilon greedy strategy within Epsilon values 0.02 and 0.1, and a drastic decline at epsilon > 0.1

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“…The basic decision tree classifier and boosted decision tree models are developed, tested and evaluated [22], [23]. The accuracies for the decision tree classifier and boosted decision tree model, when tested, are 98% and 99% respectively.…”

Section: D) Model Testing and Evaluationmentioning

confidence: 99%

A Boosted Decision Tree Model for Predicting Loan Default in P2P Lending Communities

Akanmu,

Gilal

2019

IJEAT

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Loan Default Prediction For Social Lending Is An Emerging Area Of Research In Predictive Analytics. The Need For Large Amount Of Data And Few Available Studies In The Current Loan Default Prediction Models For Social Lending Suggest That Other Viable And Easily Implementable Models Should Be Investigated And Developed. In View Of This, This Study Developed A Data Mining Model For Predicting Loan Default Among Social Lending Patrons, Specifically The Small Business Owners, Using Boosted Decision Tree Model. The United States Small Business Administration (Usba) PubliclyAvailable Loan Administration Dataset Of 27 Features And 899164 Data Instances Was Used In 80:20 Ratios For The Training And Testing Of The Model. 16 Data Features Were Finally Used As Predictors After Data Cleaning And Feature Engineering. The Gradient Boosting Decision Tree Classifier Recorded 99% Accuracy Compared To The Basic Decision Tree Classifier Of 98%. The Model Is Further Evaluated With (A) Receiver Operating Characteristics (Roc) And Area Under Curve (Auc), (B) Cumulative Accuracy Profile (Cap), And (C) Cumulative Accuracy Profile (Cap) Under Auc. Each Of These Model Performance Evaluation Metrics, Especially Roc-Auc, Showed The Relationship Between The True Positives And False Positives That Implies The Model Is A Good Fit.

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Finding an effective classification technique to develop a software team composition model

Cited by 24 publications

References 69 publications

Team Formation for Agile Software Development: A Review

Team Formation for Agile Software Development: A Review

Towards an Improved Strategy for Solving Multi-Armed Bandit Problem

A Boosted Decision Tree Model for Predicting Loan Default in P2P Lending Communities

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