The goal of software bug prediction is to identify the software modules that will have the likelihood to get bugs by using some fundamental project resources before the real testing starts. Due to high cost in correcting the detected bugs, it is advisable to start predicting bugs at the early stage of development instead of at the testing phase. There are many techniques and approaches that can be used to build the prediction models, such as machine learning. This technique is widely used nowadays because it can give accurate results and analysis. Therefore, we decided to perform a review of past literature on software bug prediction and machine learning so that we can understand better about the process of constructing the prediction model. Not only we want to see the machine learning techniques that past researchers used, we also assess the datasets, metrics and performance measures that are used during the development of the models. In this study, we have narrowed down to 31 main studies and six types of machine learning techniques have been identified. Two public datasets are found to be frequently used and object-oriented metrics are the highly chosen metrics for the prediction model. As for the performance measure, both graphical and numerical measures are often used to evaluate the performance of the models. From the results, we conclude that the machine learning technique can predict the bug, but there are not many applications in this area that exist nowadays. There are a few challenges in constructing the prediction model. Thus, more studies need to be carried out so that a well-formed result is obtained. We also provide a recommendation for future research based on the results we got from this study.
Many recent studies have shown that various multi-objective evolutionary algorithms have been widely applied in the field of search-based software engineering (SBSE) for optimal solutions. Most of them either focused on solving newly re-formulated problems or on proposing new approaches, while a number of studies performed reviews and comparative studies on the performance of proposed algorithms. To evaluate such performance, it is necessary to consider a number of performance metrics that play important roles during the evaluation and comparison of investigated algorithms based on their best-simulated results. While there are hundreds of performance metrics in the literature that can quantify in performing such tasks, there is a lack of systematic review conducted to provide evidence of using these performance metrics, particularly in the software engineering problem domain. In this paper, we aimed to review and quantify the type of performance metrics, number of objectives, and applied areas in software engineering that reported in primary studies—this will eventually lead to inspiring the SBSE community to further explore such approaches in depth. To perform this task, a formal systematic review protocol was applied for planning, searching, and extracting the desired elements from the studies. After considering all the relevant inclusion and exclusion criteria for the searching process, 105 relevant articles were identified from the targeted online databases as scientific evidence to answer the eight research questions. The preliminary results show that remarkable studies were reported without considering performance metrics for the purpose of algorithm evaluation. Based on the 27 performance metrics that were identified, hypervolume, inverted generational distance, generational distance, and hypercube-based diversity metrics appear to be widely adopted in most of the studies in software requirements engineering, software design, software project management, software testing, and software verification. Additionally, there are increasing interest in the community in re-formulating many objective problems with more than three objectives, yet, currently are dominated in re-formulating two to three objectives.
Battery capacity of mobile devices is a critical issue for developing green mobile applications. Therefore, energy efficiency has become a major concern nowadays for energy restricted embedded system such as smartphones and tablets. In industry, it is a challenge for them to develop an energy efficient product while meeting customer expectation. In previous study, there is lack of method that uses software metrics to measure power consumption of mobile applications. In this paper we have identified several software metrics that can be used as indicator to measure mobile application power consumption. The objective of this study is to which identify software metrics is suitable act as indicators to measure power consumption of mobile applications. This can help mobile software designer to measure power used for their mobile applications in the early design phase. In order to prove this concept, we randomly select two open source mobile applications as our case study. The power used of a mobile application is collected by using Trepn Profiler (Power profiling tool for Qualcomm processor CPU). We capture the actual power consumption and estimated power consumption (without calculate Android OS and profiling tool power) with the profiler. There are overall 18 available metrics based on Object-Oriented Metrics. We map the 18 metrics with the power consumption captured by Trepn Profiler. The results shown that McCabe cyclomatic complexity, number of parameters, nested block depth, weighted methods per class, number of overridden method, number of methods, total lines of code and method lines have significant relationship with power consumption of mobile application. Therefore, these eight metrics can be used as the indicator to measure mobile applications' power consumption.
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