Software maintenance is an important activity in the software lifecycle. Software maintenance does not only mean removing faults found after software release. Software needs extensions or modifications of its functions owing to changes in the business environment and software maintenance also refers to them. To help users and service suppliers benchmark work efficiency for software maintenance, and to clarify the relationships between software quality, work efficiency, and unit cost of staff, we used a dataset that includes 134 data points collected by the Economic Research Association in 2012, and analyzed the factors that affected the work efficiency of software maintenance. In the analysis, using a multiple regression model, we clarified the relationships between work efficiency and programming language and productivity factors. To analyze the influence to the quality, relationships of fault ratio was analyzed using correlation coefficients. The programming language and productivity factors affect work efficiency. Higher work efficiency and higher unit cost of staff do not affect the quality of software maintenance.

Recently, many studies have applied gamification to software engineering education and software development to enhance work results. Gamification is defined as “the use of game design elements in non-game contexts.” When applying gamification, we make various game rules, such as a time limit. However, it is not clear whether the rule affects working time or not. For example, if we apply a time limit to impatient developers, the working time may become shorter, but the rule may negatively affect because of pressure for time. In this study, we analyze with subjective experiments whether the rules affects work results such as working time. Our experimental results suggest that for the coding tasks, working time was shortened when we applied a rule that made developers aware of working time by showing elapsed time.

Critical nodes identification is of great significance in protecting power grids. Network efficiency can be used as an evaluation index to identify the critical nodes and is an indicator to quantify how efficiently a network exchanges information and transmits energy. Since power grid is a heterogeneous network and can be decomposed into small functionally-independent grids, the concept of the Giant Component does not apply to power grids. In this paper, we first model the power grid as the directed graph and define the Giant Efficiency sub-Graph (GEsG). The GEsG is the functionally-independent unit of the network where electric energy can be transmitted from a generation node (i.e., power plants) to some demand nodes (i.e., transmission stations and distribution stations) via the shortest path. Secondly, we propose an algorithm to evaluate the importance of nodes by calculating their critical degree, results of which can be used to identify critical nodes in heterogeneous networks. Thirdly, we define node efficiency loss to verify the accuracy of critical nodes identification (CNI) algorithm and compare the results that GEsG and Giant Component are separately used as assessment criteria for computing the node efficiency loss. Experiments prove the accuracy and efficiency of our CNI algorithm and show that the GEsG can better reflect heterogeneous characteristics and power transmission of power grids than the Giant Component. Our investigation leads to a counterintuitive finding that the most important critical nodes may not be the generation nodes but some demand nodes.