Table 1 - uploaded by Mazni Omar
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This paper aims to present personality types preferences among software developers in Northern region of Malaysia. Knowledge in human factor personality types is significant in order to assist project manager for making decision on the right personality types that suit into job tasks assigned to software developers. In addition, there is growing aw...
Similar publications
This paper aims to present personality types preferences among software developers in Northern region of
Malaysia. Knowledge in human factor personality types is significant in order to assist project manager for making decision on the right personality types that suit into job tasks assigned to software developers. In addition, there is growing aw...
Citations
... So, ESTP types provide great maintenance software engineers. Furthermore, (Omar et al.,2015) mentions a similar study conducted in Malaysia and in that study, INTJ, ENTJ and ISTJ types were the most prevalent ones while no ENFP types were encountered. ...
The personalities of the group members working on a software development project affect the success of the work result, depending on the suitability of the team members for their roles. Therefore, it is important to determine the roles of team members in software projects according to their personality differences. This study proposes an innovative and creative expert system for determining the appropriate roles of software engineers in software projects and talent management in the software industry. The most obvious aspect of the proposed system is that it consists of questions that do not evoke the feeling of personality analysis for those who take the test. The questions are designed to be relevant to the tasks and natural behaviors of the software engineers in their projects. Software engineers often do not believe in psychometric tests and try to manipulate them. Furthermore, the number of questions in these tests is quite large, so they can get bored. The proposed test was compared with the results of the Jung-Based Software Talent Management (JBSTM) and the Keirsey Temperament Sorter test. These two tests were applied to 20 software industry employees in a single session in a random order. A comparison questionnaire was completed after the tests. A moderate correlation between JBSTM and the Keirsey test appeared in E / I, T / F and J / P. The JBTSM and Keirsey test results were also compared in terms of test completion times. Using the results of the comparison questionnaire, the JBTSM and Keirsey test were also compared in terms of easiness, suitability, comfort and usefulness, and it was found that the JBTSM received higher scores compared to the Keirsey test in all four respects.
... Mazni et_al. conducted a study on developers' personality types to help project managers and other professionals develop strategies that enhance their teams' effectiveness [14]. In this study, we have also considered the varying roles of individual team members and their contribution to team productivity. ...
Software productivity is a multidimensional concept that is categorized as an indication of project success. For software teams, productivity is a critical aspect that needs to be considered to ensure successful software development. Several factors can affect an agile software team's during the software evolution process. However, the literature review shows that there is a lack of empirical evidence to identify these critical factors and its effect on productivity in agile software development in terms of agile teams. The aim of this work is to distinguish these factors and to specify how agile teams can have a productive impact on the software. To accomplish this, we reviewed the relevant literatureand administrated an online survey, which was administrated to 52 agile software development companies in Pakistan. The most commonly identified factors are team member and leader role, inter-team relationship, handling requirements, team velocity, conformance quality and team vision, as well as a number of host of sub-factors. The researcher used statistical techniques, such as Spearman's correlation, mean and standard deviation, to examine the results. The results showed that productivity correlates positively and significantly with most of the identified factors, except those with a negative correlation. The outcome revealed that these significant factors are critical factors to consider when examining team productivity in agile software development. The resulting quantitative analysis is expected to provide further insight into the dynamics of agile teamwork and establish a basis for further quantitative and qualitative modelling.
Context
In the light of the swift and iterative nature of Agile Software Development (ASD) practices, establishing deeper insights into capability measurement within the context of team formation is crucial, as the capability of individuals and teams can affect team performance and productivity Although a former Systematic Literature Review (SLR) synthesized the state of the art in relation to capability measurement in ASD – with a focus on selecting individuals to agile teams, and capabilities related to team performance, productivity and success determining to what degree the SLR’s results apply to practice can provide progressive insights to both research and practice.
Objective
Our study investigates how agile practitioners perceive the relevance of individual and team level measures for characterizing the capability of an agile team and its members. Here, the emphasis was also on selecting individuals to agile teams, and capabilities associated with effective teams in terms of their performance, productivity and success. Furthermore, to scrutinize variations in practitioners’ perceptions, our study further analyzes perceptions across stratified demographic groups.
Method
We undertook a Web-based survey using a questionnaire built based on the capability measures identified from a previously conducted SLR.
Results
Our survey responses (60) indicate that 127 individual and 28 team capability measures were considered as relevant by the majority of practitioners. We also identified seven individual and one team capability measure that have not been previously characterized by our SLR. The surveyed practitioners suggested that an agile team member’s responsibility and questioning skills significantly represent the member’s capability.
Conclusion
Results from our survey align with our SLR’s findings. Measures associated with social aspects were observed to be dominant compared to technical and innovative aspects. Our results can support agile practitioners in their team composition decisions.
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.
