Contributions and Publications

Throughout this work, we made several contributions and published papers to disseminate our results through the Software Engineering community. Table 8.1 presents the publications that resulted from the steps that we executed as part of this research project. We also discuss how the papers we wrote and published connect to the chapters of this text and when we executed each study, to provide an idea of the timeline for the production of this dissertation.

Table 8.1 - Publications and contributions

Written and published papers

Publication Contribution

Research proposal presented in IDoESE 2019, detailing the research objectives and methodology, published at Software Engineering Notes [22].

Description of a research methodology to investigate the use of negotiation theories and techniques to improve software project estimation.

SLM about factors affecting expert judgment estimates, published at the Journal of Systems and Software [5].

• A map organizing the factors affecting expert judgment estimates by project phase, stakeholder, and type of effect.

Written and published papers

Publication Contribution

• Identification of the research and measurement strategies that researchers employed the most.

SLM about factors affecting overconfidence and uncertainty assessments, published at the Brazilian Simposium of Software Engineering (SBES) 2021 – Research Track [238].

• Identification of factors affecting overconfidence and uncertainty assessment in software estimation.

• Identification of the research and measurement strategies that researchers employed the most.

Exploration of the SLM factors, depicting software effort estimation as more than a prediction task: as a behavioral act.

Submitted to the Empirical Software Engineering Journal.

• Description of two latent themes, representing perspectives on the factors affecting software estimates: as a technical prediction task and as a behavioral act.

• Presentation and evaluation of strength of evidence of review findings regarding the factors associated with the behavioral act theme.

Qualitative study about how software practitioners use estimates to establish commitments, published at the International Conference on Cooperative and Human Aspects of Software Engineering (CHASE) 2021 [8].

Evidence for:

• Changes in software estimates to make them defensible.

• Three different reasons for padding in the software industry: (i) contingency buffer, (ii) completing other tasks, (iii) improving overall quality.

• Padding as a tool for balancing short and long-term needs in software development and maintenance.

Description of the research problem and our proposed solution, with the results from the focus group, published at the International Conference on Software Engineering – New Ideas and Emerging Results Track (ICSE NIER) 2022 [21].

• Description of the research problem proposition, the negotiation theories, and one of the defense lenses.

• Empirical evidence on the lenses’ perceived usefulness and improvement opportunities.

Complete description of the artifact (defense lenses and digital simulation) and of the controlled experiment. Accepted for publication at the International Conference on Software Engineering – Technical Track 2023.

• Description of the digital simulation and the defense lenses, their theoretical background, and their rationale.

• Empirical evidence about the intentions of software practitioners to adopt the defense lenses.

• Empirical evidence on the digital simulation’s and lenses’ perceived usefulness.

First, we presented our research proposal at IDoESE (International Doctoral Symposium on Empirical Software Engineering) 2019 [22]. We described our research problem and the

approach that we envisioned, together with our research methodology—which corresponds to a lot of what we discussed in CHAPTER 1.

Next, we described the studies that we carried out to understand deeper our research problem, namely an SLM and a qualitative study in the software industry. We executed the SLM from the end of the year 2018 to the mid of the year 2020. The results from the SLM showed how pressure over the estimates is among the many relevant factors affecting expert judgment estimates and how removing padding from the estimates can harm their accuracy. We explored these results in CHAPTER 2 and wrote a paper about it, accepted for publication at the Journal of Systems and Software in the end of 2021.

Moreover, our SLM inspired us to execute a secondary SLM about the factors affecting overconfidence and the uncertainty assessment of estimates, specific aspects of estimates’

accuracy. We found a total of eight factors. We published it at the SBES (Simpósio Brasileiro de Engenharia de Software - Brazilian Symposium on Software Engineering) 2021. We did not include the paper results in this dissertation because it is not entirely aligned with our proposed research question.

We also submitted an additional paper to the Empirical Software Engineering Journal, with an exploration of the SLM factors. This paper depicts software effort estimation as more than a prediction task: as a behavioral act. We also did not include the paper results in this dissertation because it is not entirely aligned with our proposed research question.

We executed the qualitative study from the mid of 2019 to the mid of 2020. Its results reinforced the evidence on how pressure affects estimates, leading estimators to change their estimates to make them defensible and to use padding as a tool to balance short and long-term needs in software development and maintenance [8]. Therefore, padding is a strategy for dealing with pressure in the software industry. We explored these results in CHAPTER 4 and published them at CHASE (Cooperative and Human Aspects of Software Engineering) 2021 [8].

This dissertation also described the solution that we propose together with its theoretical foundations. We resorted to negotiation methods introduced in CHAPTER 2, looking for an alternative that could allow estimators to defend their estimates and negotiate effectively for realistic commitments instead of yielding to pressure, changing their estimates, or pad other tasks to compensate. Therefore, we devised the defense lenses as CHAPTER 5 explains. We worked on the first version of our artifact from the mid of the year 2019 to the beginning of the year 2021.

Next, CHAPTER 6 presents the details of a focus group to evaluate our proposed approach. Our evaluation strategy relied on experts’ opinions on the usefulness and usability of the defense lenses. We worked on it for about three months starting at the mid of the year 2021.

We described the defense lenses and the results from the focus group in a paper published at the International Conference on Software Engineering – New Ideas and Emerging Results Track (ICSE NIER) 2022.

Right after the focus group, we worked on refining the artifact, by improving the defense lenses and crafting the digital simulation in line with the improvement opportunities revealed in the focus group. We evaluated the artifact with a controlled experiment. We started planning the controlled experiment at the mid of the year 2021. We finished its data collection and analysis in the mid of the year 2022. CHAPTER 7 presents the results of this study, accepted for publication at the International Conference on Software Engineering – Technical Track 2023.

In addition to the individual contributions of each chapter and publication abovementioned, we also provide additional contributions to the Software Engineering community. First, the map of factors affecting expert-judgment software estimates resulting from our SLM is comprehensive enough to benefit other researchers investigating topics related to software effort estimation. Second, our approach incorporates principles from negotiation, thus holding the potential to promote the learning of this soft skill among software practitioners.

Third, we propose an approach to deal with the behavior of stakeholders, including estimators—

a step towards Behavioral Software Engineering, i.e., to the “study of cognitive, behavioral and social aspects of software engineering performed by individuals, groups or organizations” [38].

Fourth, we incorporated to our analysis the Theory of Planned Behavior, a consolidated social science theory, showing how it can be useful to understand behavior change in our field.