Abstract Effort estimation is an important activity in agile software development. The goal of the presented study was to determine the influence of individual competence on software development effort estimation. In particular, we measured both the accuracy of effort estimation and the duration of the estimation process itself, both for three different estimation methods. The subjects of our study were teams of students of a graduate‐level software engineering course at the University of Ljubljana, Faculty of Computer and Information Science. Based on the grades that individual students attained in their undergraduate study, we classified each team as “high‐competence” or “low‐competence” and additionally as “heterogeneous” or “homogeneous” (the criterion here being the variance of the members' average grades). We found out that there was no significant difference in effort estimation accuracy neither between high‐competence and low‐competence teams nor between heterogeneous and homogeneous teams, regardless of which estimation method was used. However, high‐competence teams spent significantly less time on effort estimation than low‐competence ones. Likewise, for two of the employed estimation methods, heterogeneous teams completed effort estimation in a significantly shorter time than homogeneous teams. These results might benefit both academic and professional community.

Published studies on agile effort estimation predominantly focus on comparisons of the accuracy of different estimation methods, while efficiency comparisons, i.e. how much time the estimation methods consume was not in the forefront. However, for practical use in software development, the time required can be a very important cost factor for enterprises, especially when the accuracy of different agile effort estimations is similar. In this study, we thus try to advance the current standard accuracy comparison between methods by introducing efficiency, i.e. time it takes to use a method as an additional dimension of comparison. We conduct this comparison between three agile effort estimation methods that were not yet compared in the literature, namely, Planning Poker, Bucket System and Affinity Estimation. For the comparison, we used eight student teams with 29 students who had to use all the effort estimation methods during the course where they had to finish a programming project in 3 weeks. The results indicate that after the students get used to using the different methods the accuracy between them is not statistically significantly different, however, the efficiency is. On average, Bucket System and Affinity Estimation methods take half as much time as Planning Poker.

Many practical problems are modeled with networks and graphs.Their exploration is of significant importance, and several graph-exploration algorithms already exist.In this paper, we focus on a type of vertex equivalence, called exploratory equivalence, which has a great potential to speed up such algorithms.It is an equivalence based on graph automorphisms and can, for example, help us in solving the subgraph isomorphism problem, which is a well-known NP -hard problem.In particular, if a given pattern graph has nontrivial automorphisms, then each of its nontrivial exploratory equivalent classes gives rise to a set of constraints to prune the search space of solutions.In the paper, we define the maximum exploratory equivalence problem.We show that the defined problem is at least as hard the graph isomorphism problem.Additionally, we present a polynomial-time algorithm for solving the problem when the input is restricted to tree graphs.Furthermore, we show that for trees, a maximum exploratory equivalent partition leads to a globally optimal set of subgraph isomorphism constraints, whereas this is not necessarily the case for general graphs.

Motivated by improving the efficiency of pattern matching on graphs, we define a new kind of equivalence on graph vertices.Since it can be used in various graph algorithms that explore graphs, we call it exploratory equivalence.The equivalence is based on graph automorphisms.Because many similar equivalences exist (some also based on automorphisms), we argue that this one is novel.For each graph, there are many possible exploratory equivalences, but for improving the efficiency of the exploration, some are better than others.To this end, we define a goal function that models the reduction of the search space in such algorithms.We describe two greedy algorithms for the underlying optimization problem.One is based directly on the definition using a straightforward greedy criterion, whereas the second one uses several practical speedups and a different greedy criterion.Finally, we demonstrate the huge impact of exploratory equivalence on a real application, i.e., graph grammar parsing.