Agile Software Development (ASD) has become the mainstream software development method of choice. Its core fundamentals are based on Teamwork factors and the higher value of individuals and their interactions over processes and tools. However, there is no common understanding regarding the factors that should be considered for defining an ASD Teamwork construct. Driven by this problem, we present a thematic network that synthesizes the information presented in the literature, and eases knowledge sharing by defining a terminology. The thematic network is the result of the following process: (i) studies definition to be used as data source through a literature review; (ii) data extraction from these studies; (iii) data translation into codes; (iv) codes translation into themes; (v) creation of higher-order themes model; and (vi) assessment of synthesis trustworthiness. The resulting thematic network comprises four higher-order themes: Cohesion, Orientation, Shared Leadership, and Autonomy. We also evaluate the applicability of the identified themes in ASD Teamwork constructs in the literature. We concluded that the constructed thematic network can be generalized to ASD, and used as basis by researchers who intend to explore ASD Teamwork. Further, practitioners can use our results to understand agile teams’ dynamics better and improve their efficiency.

Agile Software Development (ASD) has become the mainstream software development method of choice.Its core fundamentals are based on Teamwork factors and the higher value it gives to individuals and their interactions over processes and tools.Teamwork and human factors have been addressed as essential topics in the literature, and researchers have stated the importance of measuring it to increase the chances of success of ASD projects.However, there is no common understanding regarding the factors that should be considered for defining an ASD Teamwork construct.Driven by this problem, this paper presents a thematic network that defines the themes (i.e., factors) that should be considered when addressing ASD Teamwork.The ASD Teamwork thematic network is the result of a process that consisted of (i) defining the studies used as a data source through a literature review; (ii) extracting data from these studies; (iii) translating this data into codes; (iv) translating the codes into themes; (v) creating the model of higher-order themes; and, (vi) assessing the trustworthiness of the synthesis.The resulting thematic network comprises four higher-level themes: Cohesion, Orientation, Shared Leadership, and Autonomy.We believe that the constructed thematic network can be generalized to ASD and used as the basis by researchers who intend to explore ASD Teamwork.Further, practitioners can use our results to understand agile teams' dynamics better and improve their efficiency.

Context: Companies must make a paradigm shift in which both short- and long-term value aspects are employed to guide their decision-making. Such need is pressing in innovative industries, such as ICT, and is the core of Value-based Software Engineering (VBSE). Objective: This paper details three case studies where value estimation models using Bayesian Network (BN) were built and validated. These estimation models were based upon value-based decisions made by key stakeholders in the contexts of feature selection, test cases execution prioritization, and user interfaces design selection. Methods: All three case studies were carried out according to a Framework called VALUE — improVing decision-mAking reLating to software-intensive prodUcts and sErvices development. This framework includes a mixed-methods approach, comprising several steps to build and validate company-specific value estimation models. Such a building process uses as input data key stakeholders’ decisions (gathered using the Value tool), plus additional input from key stakeholders. Results: Three value estimation BN models were built and validated, and the feedback received from the participating stakeholders was very positive. Conclusions: We detail the building and validation of three value estimation BN models, using a combination of data from past decision-making meetings and also input from key stakeholders.

Search-Based software engineering (SBSE) deals with metaheuristic search-based optimization techniques to provide solutions for complex problems.A popular problem in literature is the team formation problem (TFP), which consists of finding the best allocation of human resources to a software development project.This problem is recognized as NP-hard and it is more complex in companies that carry out multiple projects.This paper presents an effective and automated approach to allocate multiple developers into multiple teams to maximize the technical compatibility between them.The approach consists of an SBSE method that uses Genetic Algorithm to simultaneously build multiple teams, using data from tag-based profiles.We conducted an empirical evaluation using data from eight realworld software projects of a Brazilian company.The results indicate that tag-based profiles is a promising information source to represent technical knowledge, since the suggested teams were considered to have the proper skills to the attend the technical demand of the projects.The approach was able to reach high levels of satisfaction, delivering teams in an effective and automated way.Although, further investigation needs to be conducted to reach stronger conclusions.

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.

One of the key challenges in constructing a Bayesian network BN is defining the node probability tables (NPT).For large-scale BN, learning NPT through domain experts knowledge elicitation is unfeasible.Previous works proposed solutions to this problem using the concept of ranked nodes; however, they have limited modeling capabilities or rely on BN experts to apply them, reducing their applicability.In this paper, we present an expert system based on production rules to define NPTs with the purpose of enabling the definition of NPTs by experts with no ranked nodes-specific knowledge.To create the rules, we elicited data from an expert in ranked nodes.To validate our approach, we executed an experiment with a BN already published in the literature to verify if, with our approach, a practitioner can achieve the same or better configuration for the NPTs.We used the Brier score to assess the NPTs accuracy and evaluated the results with the Wilcoxon test.All the Wilcoxon tests executed rejected the null hypotheses that stated that the Brier scores for the original NPTs method were the same as the new NPTs.By using our solution, a practitioner can accurately define NPTs without understanding the concept of ranked nodes.

Managing software development projects is a complex task because it requires organizing and monitoring several activities.Recently, in order to assist on software projects management, artifact-based models were proposed in the literature.However, the current solutions do not present means to monitor projects health and assist on decisions making.Due to the recent popularization of agile methods, they are the units of study of this research.In this work, we present a method to build artifact and measurementbased models to assess agile projects health.We applied the method to build a generic model based on industrys best practice.We defined the models artifacts and metrics based on findings of a literature review and the assistance of an expert.For each models artifact, we applied the Goal-Question-Metric paradigm to define the metrics.Afterwards, from the GQM meta-model, we constructed a Bayesian network.We validated the model with simulated scenarios.Given the successful results, we concluded that the method and model are promising.