Impact of personality and gender diversity on software development teams' performance

Gila, Abdul Rehman; Jaafa, Jafreezal; Omar, Mazni; Tunio, Muhammad Zahid

doi:10.1109/i4ct.2014.6914186

Cited by 32 publications

(32 citation statements)

References 17 publications

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“…This is because more ideas, suggestions, and opinion are generated during the learning process and thus encouraging team members to have good coordination and communication. Previous studies highlighted that extrovert members are most likely to perform better compared to other personality types [24,25]. However, in teamwork, a balanced combination of personality types, introvert (I)-extrovert (E), sensing (S)-intuitive (N), feeling (F)-thinking (T), judging (J)-perceiving (P), is essential.…”

Section: Resultsmentioning

confidence: 97%

Finding the Effectiveness of Software Team Members Using Decision Tree

Omar

Syed-Abdullah

2015

Pattern Analysis, Intelligent Security and the Internet of Things

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This paper presents steps taken in finding the effectiveness of software team members using decision tree technique. Data sets from software engineering (SE) students were collected to establish pattern relationship among four predictor variables-prior academic achievements, personality types, team personality diversity, and software methodology-as input to determine team effectiveness outcome. There are three main stages involved in this study, which are data collection, data mining using decision tree, and evaluation stage. The results indicate that the decision tree technique is able to predict 69.17 % accuracy. This revealed that the four predictor variables are significant and thus should consider in building a team performance prediction model. Future research will be carried to obtain more data and use a hybrid algorithm to improve the model accuracy. The model could facilitate the educators in developing strategic planning methods in order to improve current curriculum in SE education.

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Section: Resultsmentioning

confidence: 97%

Finding the Effectiveness of Software Team Members Using Decision Tree

Omar

Syed-Abdullah

2015

Pattern Analysis, Intelligent Security and the Internet of Things

Self Cite

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“…However, this problem is still persistent in the field of software development since few researchers have ever tried to test personality and gender collectively to test the suitability of the team handy for software development. In this regard, Richards and Busch [28], Gilal et al, [10], and Rehman et al, [29] also assert that maturity level is yet to find in software development research. In the same vein, Trauth [30] also recommends that the need of improvement is required in the theoretical work on software development.…”

Section: B Gender and Software Developmentmentioning

confidence: 99%

“…According to Furham [21] both MBTI and Big Five personality tests are helpful when a researcher aims to examine behavioral and cognitive sides of individuals by correlating both the scales. However, there are many proponents of MBTI in the domain of software engineering as this theory has been widely used in the past research studies [10], [22]- [27]. Thus, keeping in view the wide acceptance of MBTI in terms of its effectiveness, the current study has used this theory.…”

Section: A Personality and Software Developmentmentioning

confidence: 99%

“…Study conducted by Gilal et al, [10] comes among the few studies which focused personality with gender. This study investigated the performance variation among software development team members caused by genders" personality types.…”

Section: B Gender and Software Developmentmentioning

confidence: 99%

“…It has certain natural equations which form interpersonal and mental skills. It is highlighted because it was found that certain personality types are flexible to work with each other and some are not [9], [10]. Certain studies have been conducted in the past which proposed several methods and models for finding effective personnel for programming by focusing personality.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Suitable Personality Traits for Learning Programming Subjects: A Rough-Fuzzy Model

Gilal¹,

Jaafar²,

Omar³

et al. 2017

ijacsa

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Abstract-Programming is a cognitive activity which requires logical reasoning to code for abstract presentation. This study aims to find out the personality traits of students who maintain the effective grades in learning programming courses such as structured programming (SP) and object oriented programming (OOP) by gender classification. Data were collected from three universities to develop, validate, and generalize the Rough-Fuzzy model. Genetic and Johnson algorithms were applied under Rough set theory's (RST) principles to extract the decision rules. In addition, Standard Voting, Naïve Bayesian, and Object Tracking procedures were applied on the generated decision rules to find the prediction accuracy of each algorithm. Mamdani's Fuzzy Inference System (FIS) was used for mapping the decision rules' condition (input) to decision (output) based on fuzzy set theory (FST) to develop the model. The results highlighted that certain personality compositions can be suitable for scoring good grades in programming subjects. For instance, a female student is capable enough to improve the programming skills if she is composed of introvert and sensing personality traits. Therefore, it is important to investigate an appropriate personality composition for programming learners.

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

Gilal

Jaafar

Capretz

et al. 2017

J Software Evolu Process

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Ineffective software team composition has become recognized as a prominent aspect of software project failures. Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection. It is also believed that the technique/s used while developing a model can impact the overall results. Thus, this study aims to (1) discover an effective classification technique to solve the problem and (2) develop a model for composition of the software development team. The model developed was composed of 3 predictors: team role, personality types, and gender variables; it also contained 1 outcome: team performance variable. The techniques used for model development were logistic regression, decision tree, and rough sets theory (RST). Higher prediction accuracy and reduced pattern complexity were the 2 parameters for selecting the effective technique. Based on the results, the Johnson algorithm (JA) of RST appeared to be an effective technique for a team composition model. The study has proposed a set of 24 decision rules for finding effective team members. These rules involve gender classification to highlight the appropriate personality profile for software developers. In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models.

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Impact of personality and gender diversity on software development teams' performance

Cited by 32 publications

References 17 publications

Finding the Effectiveness of Software Team Members Using Decision Tree

Finding the Effectiveness of Software Team Members Using Decision Tree

Suitable Personality Traits for Learning Programming Subjects: A Rough-Fuzzy Model

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

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