TY - GEN
T1 - SwEDeL
T2 - 22nd Brazilian Symposium on Software Quality, SBQS 2023
AU - Matsubara, Patricia Gomes Fernandes
AU - Steinmacher, Igor
AU - Gadelha, Bruno
AU - Conte, Tayana
N1 - Publisher Copyright: © 2023 ACM.
PY - 2023/11/7
Y1 - 2023/11/7
N2 - Software organizations face increasing pressure for higher productivity and faster delivery. In this context, technically sound software estimates created by competent software practitioners to account for software risks can be rejected, favoring too aggressive estimates and the unrealistic commitments that arise from them. Then, software teams work under constant time pressure, sacrificing product quality to keep up with the expectations created. Time pressure also leads software practitioners to exhibit emotional distress, decreasing productivity, which leads to more time pressure and delays: a vicious cycle. This work proposes an approach to support software estimators in defending their realistic estimates instead of yielding to pressure over them. We designed a set of defense lenses based on consolidated negotiation principles and presented them through a digital simulation. We evaluated the digital simulation through a controlled experiment with software professionals. We employed the Theory of Planned Behavior to understand the intentions of participants to defend their software estimates, also collecting data on the antecedents of intentions: attitudes, subjective norms, and perceived behavioral control. Our results show that scores for the study variables improved among experimental group participants after participating in the digital simulation. They were more inclined to choose a defense action when facing pressure scenarios than the control group. The participants also perceived the lenses as useful, showing the relevance of the proposed approach. The original paper was published in Matsubara et al. [17].
AB - Software organizations face increasing pressure for higher productivity and faster delivery. In this context, technically sound software estimates created by competent software practitioners to account for software risks can be rejected, favoring too aggressive estimates and the unrealistic commitments that arise from them. Then, software teams work under constant time pressure, sacrificing product quality to keep up with the expectations created. Time pressure also leads software practitioners to exhibit emotional distress, decreasing productivity, which leads to more time pressure and delays: a vicious cycle. This work proposes an approach to support software estimators in defending their realistic estimates instead of yielding to pressure over them. We designed a set of defense lenses based on consolidated negotiation principles and presented them through a digital simulation. We evaluated the digital simulation through a controlled experiment with software professionals. We employed the Theory of Planned Behavior to understand the intentions of participants to defend their software estimates, also collecting data on the antecedents of intentions: attitudes, subjective norms, and perceived behavioral control. Our results show that scores for the study variables improved among experimental group participants after participating in the digital simulation. They were more inclined to choose a defense action when facing pressure scenarios than the control group. The participants also perceived the lenses as useful, showing the relevance of the proposed approach. The original paper was published in Matsubara et al. [17].
KW - Behavioral Software Engineering
KW - Defense of Estimates
KW - Negotiation
KW - Software Effort Estimation
UR - http://www.scopus.com/inward/record.url?scp=85180158372&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85180158372&partnerID=8YFLogxK
U2 - 10.1145/3629479.3629498
DO - 10.1145/3629479.3629498
M3 - Conference contribution
T3 - ACM International Conference Proceeding Series
SP - 352
EP - 354
BT - SBQS 2023 - Proceedings of the 22nd Brazilian Symposium on Software Quality
PB - Association for Computing Machinery
Y2 - 7 November 2023 through 10 November 2023
ER -