Abstract Information technology consumes up to 10% of the world's electricity generation, contributing to CO2emissions and high energy costs. Data centers, particularly databases, use up to 23% of this energy. Therefore, building an energy‐efficient (green) database engine could reduce energy consumption and CO2emissions. The goal of this study is to understand the factors driving databases' energy consumption and execution time throughout their evolution. We conducted an empirical case study of energy consumption by 2 MySQL database engines, InnoDB and MyISAM, across 40 releases. We examined the relationships of 4 software metrics to energy consumption and execution time to determine which metrics reflect the greenness and performance of a database. Our analysis shows that database engines' energy consumption and execution time increase as databases evolve. Moreover, the lines of code (LOC) metric is correlated moderately to strongly with energy consumption and execution time in 88% of cases. Our findings provide insights to practitioners and researchers. Database administrators may use them to select a fast, green release of the MySQL database engine. MySQL developers may use LOC to assess products' greenness and performance. Researchers may use our findings to further develop new hypotheses or build models predicting greenness and performance of databases.

Certain software defects require corrective changes repeatedly in a few components of the system. One type of such defects spans multiple components of the system, and we call such defects pervasive multiple-component defects (PMCDs). In this paper, we describe an empirical study of six releases of a large legacy software system (of approx. size 20 million physical lines of code) to analyze PMCDs with respect to: (1) the complexity of fixing such defects and (2) the persistence of defect-prone components across phases and releases. The overall hypothesis in this study is that PMCDs inflict a greater negative impact than do other defects on defect-correction efficacy. Our findings show that the average number of changes required for fixing PMCDs is 20-30 times as much as the average for all defects. Also, over 80% of PMCD-contained defect-prone components still remain defect-prone in successive phases or releases. These findings support the overall hypothesis strongly. We compare our results, where possible, to those of other researchers and discuss the implications on maintenance processes and tools.

In this paper, we overview a new approach to comparing execution traces. Such comparison can be useful for purposes such as improving test coverage and profiling system's users. In our approach, traces are compressed into different levels of compaction and are then compared iteratively from highest to lowest levels, rejecting dissimilar traces in the process and eventually leaving residual, similar traces. These residual traces form an important feedback for improvement or analysis goals. The preliminary results show that the approach is scalable for industrial use.

This paper discusses the Pareto principle as it relates to the distribution of software defects in code. We look at evidence in the context of both the software test team, and users of the software. We also investigate two related principles. The first principle is that the distribution of defects in code relates to the distribution of complexity in code. The second principle is that how we define complexity relates to the distribution of defects in code. We present this work as an empirical study of three general hypotheses investigated for large production-level software; we show that the essence of the principle holds, while precise percentages do not.