Over the years, researchers have explored the influence of human factors in software engineering, showing that the team members' personalities might affect teamwork.However, it is challenging to measure software engineers' personalities due to the number of available psychometric instruments and the possibility of using different scales and classifications.Our study compares the personality traits measured by three psychometric instruments used in Software Engineering: Big Five Inventory (BFI), 16 Personality Factors (16PF), and Context Cards (CC).For this purpose, we executed an empirical study in which we collected data from 29 software developers for each of the evaluated instruments.As a result, we identified a moderate correlation between BFI and 16PF, confirming the current stateof-the-art.For the remaining combinations, there was a weak correlation.As implications for this research, there is a need to empirically evaluate BFI and CC (context-specific survey) in terms of construct validity since they have moderate to low correlation.

Using knowledge-based systems for helping agile teams to improve their performance is not a fact in the industry.In previous work, we have presented Kaizen, a knowledge-based Bayesian network for assisting Scrum teams in diagnosing their value stream in light of the predicted Customer Satisfaction and, consequently, improve their performance.This study assesses Kaizen's accuracy to predict Customer Satisfaction using realworld data.We adopted Kaizen for one software development company and collected data from 18 projects using an online questionnaire.We collected two types of data: inputs for Kaizen and the expected Customer satisfaction.We used the first type of collected data as inputs for Kaizen to calculate the predicted Customer satisfaction.Then, we assessed Kaizen's accuracy by comparing the predicted (i.e., calculated) and expected (i.e., collected) Customer satisfaction using face value and the average Brier score.Considering the face value, Kaizen predicted Customer Satisfaction correctly for 14 out of the 18 projects.The average Brier Score was 0.16.The model predicts, with satisfactory accuracy, the Customer Satisfaction and systemizes the process for Scrum teams to self-diagnose, enabling for causal analysis and supporting their continuous improvement.

Background: The focus of Agile software development (ASD) is different than plan-driven development, requiring new software process improvement (SPI) paradigms.Objective: To identify and synthesize the possible gaps of Agile improvement solutions (AIS) given their focus on people factors, report of successful adoption in industry projects and availability of tool support.Method: We applied a Systematic Literature Review of studies published up to (and including) 2017 through backward and forward snowballing given a start set.Results: In total, we evaluated 55 papers, of which 44 included AIS and the main findings are: 1) 26 consider teamwork factors; 2) 21 were applied on industry; 3) 10 out of these 21 presented evidence of increase in company performance; and 4) 19 of the solutions are for the purpose of adoption, 18 for assessment and 8 are maturity models.Conclusion: The main implication for this research is a need for more and better empirical studies documenting and evaluating AIS.For the industry, the review provides a map of current AIS approaches and can be used as a starting point to adopt agile SPI.