Software project teams often plan and replan their development processes to accommodate a project’s particularities and volatility. This continual planning is called software process tailoring (SPT). In SPT, software teams often experiment with innovative process solutions to address changes. Because mistakes are inevitable in development and innovation, learning from failure is critical for team members to acknowledge, reflect on, and rectify process mistakes. This article focuses on failure-based learning behavior (FLB) in SPT and explores how a team’s FLB can be facilitated to foster SPT performance. Based on social cognitive theory, three team cognitive factors, self-efficacy, self-esteem, and identification, are examined. The research also explores the moderating role of servant leadership in the effects of these factors on FLB. We used a survey research method and collected data from 65 software teams to test the model. The results show that a team’s FLB significantly fosters SPT performance. Higher team self-efficacy and team identification increase team FLB, whereas higher team self-esteem decreases FLB. Servant leadership significantly moderates the effects of team self-esteem and identification on FLB, but this leadership does not have a significant effect on the relationship between team self-efficacy and FLB.

Software process improvement (SPI) is critical to information system development. In the context of successful SPI, this research focuses on a firm's dynamic learning ability to see how it facilitates an effective means of acquiring and utilizing external SPI knowledge in responding to changing software development environments. Specifically, the authors propose a research model to investigate how two mechanisms of absorptive capacity are incorporated with innovation culture as a contextual factor to enable successful software process improvement. A survey was conducted including 125 SPI certified firms in China and Taiwan to examine the model. The findings indicate that a firm's potential absorptive capacity significantly influences realized absorptive capacity, which has a significant impact on SPI success and acts as a partial mediator between potential absorptive capacity and SPI success. Moreover, the results suggest that the mediating effect of potential absorptive capacity on SPI success via realized absorptive capacity is amplified when innovation culture is imposed.

This study examines the relationship between software project team characteristics and team performance by focusing on two questions: whether or not different team characteristics in software project team affect project performance, and whether or not different types of team motivation lead to different level of influence to the relationship between team characteristics and performance? In this study, we argue that team motivation should be considered as a moderate between the relationship of team characteristics and performance. Different kind and level of team motivation can result in different project performance outcome. We examine the interaction effects between team characteristics and team motivation before their final impact to the performance. The results show that the relationship between project team characteristics and performance can be interfered with team motivation. Management should pay more attention to team motivation within project teams so that a better project performance could be achieved.

In this paper, we present a novel fault detection and fault diagnosis technique for field programmable gate arrays (FPGAs). The cell is configured to implement a bijective function to simplify the testing of the whole cell array. The whole chip is partitioned into disjoint one-dimensional arrays of cells. The input patterns can be easily generated with a k-bit binary counter. According to the characteristics of the bijective cell function, a novel built-in self-test structure is also proposed. To locate a faulty CLB (configurable logic block), two diagnosis sessions are required. However, the maximum number of configurations is k+4 for diagnosing a faulty CLB. The diagnosis complexity of our approach is also analyzed. Our results show that the time complexity is independent of the array size of the FPGA. In other words, we can make the FPGA array C-diagnosable with our approach.