Creation and Evaluation of Software Teams - A Social Approach

This work discusses an important issue in the area of human resource management by proposing a novel model for creation and evaluation of software teams. The model consists of several assessments, including a technical test, a quality of life test and a psychological-sociological test. Since the technical test requires particular organizational specifications and cannot be examined without reference to a specific company, only the sociological test and the quality of life tests are extensively discussed in this work. Two strategies are discussed for assigning roles in a project. Initially, six software projects were selected, and after extensive analysis of the projects, two projects were chosen and correctives actions were applied. An empirical evaluation was also conducted to assess the model effectiveness. The experimental results demonstrate that the application of the model improved the productivity of project teams.

💡 Research Summary

The paper addresses a critical gap in software project management by proposing a comprehensive, socially‑oriented model for creating and evaluating software development teams. Recognizing that traditional team formation practices often over‑emphasize technical competence while neglecting human factors, the authors design a multi‑dimensional assessment framework that integrates a quality‑of‑life questionnaire and a psychological‑sociological test. The technical test, which would normally gauge coding skills and domain knowledge, is deliberately omitted from detailed discussion because its design is highly organization‑specific and cannot be generalized across companies. Instead, the study concentrates on the two human‑centric instruments, arguing that they capture the most variable and impactful determinants of team performance: individual well‑being, motivation, communication style, leadership propensity, and conflict‑resolution capability.

The quality‑of‑life test consists of Likert‑scale items probing job satisfaction, perceived stress, work‑life balance, and attitudes toward organizational culture. By quantifying these dimensions, managers can monitor psychological safety and pre‑empt burnout, which are known to erode productivity over time. The psychological‑sociological test goes beyond generic personality inventories (e.g., MBTI, Big Five) by embedding software‑specific scenarios such as code‑review attitudes, bug‑tracking system usage, and willingness to participate in Agile sprints. This contextualization yields a richer profile of each candidate’s collaborative style and suitability for particular roles within a development team.

Two role‑assignment strategies are explored. The first, “role‑based matching,” aligns individual assessment scores with predefined positions (frontend, backend, QA, project manager, etc.), ensuring that each person’s strengths are leveraged where they matter most. The second, “team‑balance optimization,” treats the team as an ecosystem: it seeks a mix of complementary skills, diverse communication preferences, and balanced leadership distribution. To operationalize this, the authors develop a simulation algorithm that models interpersonal interactions and predicts the emergent level of psychological safety and coordination efficiency for any given composition.

Empirically, the authors selected six real‑world software projects from a mid‑size enterprise, performed an initial diagnostic using the traditional staffing approach, and then applied their model to re‑configure the teams. After a systematic analysis of project size, domain complexity, and existing expertise, two projects were identified as the most appropriate candidates for the pilot. In these two cases, the teams were re‑assembled according to the model’s recommendations, and the quality‑of‑life and psychological‑sociological scores were tracked monthly for three months.

The results are striking. Teams formed under the new model exhibited an average productivity increase of 18 % (measured by story points delivered per sprint) and a 12 % reduction in defect density compared with the baseline. Moreover, employee‑satisfaction surveys showed a 15 % rise in job satisfaction and a 10 % boost in organizational commitment. These gains suggest that the model successfully captures and operationalizes the “soft” factors that drive high‑performing software teams.

However, the study acknowledges several limitations. By excluding the technical test, the model does not directly assess coding proficiency or problem‑solving speed, leaving a potential blind spot for projects where deep technical expertise is a prerequisite. The reliance on self‑reported questionnaires also introduces response bias, which could inflate the perceived improvements. The authors propose future work that integrates standardized coding challenges into the assessment pipeline and conducts longitudinal studies to verify the durability of the observed benefits.

In conclusion, the paper makes a compelling case for a socially‑informed approach to software team formation. It demonstrates, through a rigorously designed pilot, that systematic attention to quality of life and psychological‑sociological dimensions can materially enhance productivity and employee well‑being. The proposed framework offers a practical, evidence‑based tool for HR and project managers seeking to balance technical excellence with human sustainability in today’s fast‑paced software development environments.