Reviews studies from 1991-2013 to assess application of ML techniques for SFP.Identifies seven categories of the ML techniques.Identifies 64 studies to answer the established research questions.Selects primary studies according to the quality assessment of the studies.Systematic literature review performs the following:Summarize ML techniques for SFP models.Assess performance accuracy and capability of ML techniques for constructing SFP models.Provide comparison between the ML and statistical techniques.Provide comparison of performance accuracy of different ML techniques.Summarize the strength and weakness of the ML techniques.Provides future guidelines to software practitioners and researchers. BackgroundSoftware fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults. MethodIn this study we perform a systematic review of studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the statistical techniques and other machine learning techniques. Further the strengths and weaknesses of machine learning techniques are summarized. ResultsIn this paper we have identified 64 primary studies and seven categories of the machine learning techniques. The results prove the prediction capability of the machine learning techniques for classifying module/class as fault prone or not fault prone. The models using the machine learning techniques for estimating software fault proneness outperform the traditional statistical models. ConclusionBased on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers. However, the application of the machine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.

A systematic review of machine learning techniques for software fault prediction

The objective of this paper is to present the current evidence relative to the effectiveness of pair programming (PP) as a pedagogical tool in higher education CS/SE courses. We performed a systematic literature review (SLR) of empirical studies that investigated factors affecting the effectiveness of PP for CS/SE students and studies that measured the effectiveness of PP for CS/SE students. Seventy-four papers were used in our synthesis of evidence, and 14 compatibility factors that can potentially affect PP's effectiveness as a pedagogical tool were identified. Results showed that students' skill level was the factor that affected PP's effectiveness the most. The most common measure used to gauge PP's effectiveness was time spent on programming. In addition, students' satisfaction when using PP was overall higher than when working solo. Our meta-analyses showed that PP was effective in improving students' grades on assignments. Finally, in the studies that used quality as a measure of effectiveness, the number of test cases succeeded, academic performance, and expert opinion were the quality measures mostly applied. The results of this SLR show two clear gaps in this research field: 1) a lack of studies focusing on pair compatibility factors aimed at making PP an effective pedagogical tool and 2) a lack of studies investigating PP for software design/modeling tasks in conjunction with programming tasks.

https://researchbank.swinburne.edu.au/file/0de976cc-8dd1-4337-855f-3a95bbe71eb0/1/PDF (Accepted manuscript).pdf

Empirical Studies of Pair Programming for CS/SE Teaching in Higher Education: A Systematic Literature Review

PhysioBank是一个大型的逐渐扩增的生理学信号和相关数据的数字化记录文档。目前包含多参数的心肺、神经和其他生物医学信号，尤以心电图（ECG）为主。信号来自健康受试者和各种疾病的患者。涉及的疾病包括心脏猝死、充血性心力衰竭、癫痫、步态不稳、睡眠呼吸暂停和衰老等。

PhysioBank

SUMMARY

Software change impact analysis (CIA) is a technique for identifying the effects of a change, or estimating what needs to be modified to accomplish a change. Since the 1980s, there have been many investigations on CIA, especially for code-based CIA techniques. However, there have been very few surveys on this topic. This article tries to fill this gap. And 30 papers that provide empirical evaluation on 23 code-based CIA techniques are identified. Then, data was synthesized against four research questions. The study presents a comparative framework including seven properties, which characterize the CIA techniques, and identifies key applications of CIA techniques in software maintenance. In addition, the need for further research is also presented in the following areas: evaluating existing CIA techniques and proposing new CIA techniques under the proposed framework, developing more mature tools to support CIA, comparing current CIA techniques empirically with unified metrics and common benchmarks, and applying the CIA more extensively and effectively in the software maintenance phase. Copyright © 2012 John Wiley & Sons, Ltd.

/pdf/a-survey-of-code-based-change-impact-analysis-techniques-54abmu8whg.pdf

A survey of code‐based change impact analysis techniques

Artificial intelligence (AI) has transformed the world and the relationships among humans as the learning capabilities of machines have allowed for a new means of communication between humans and machines. In the field of health, there is much interest in new technologies that help to improve and automate services in hospitals. This article aims to explore the literature related to conversational agents applied to health care, searching for definitions, patterns, methods, architectures, and data types. Furthermore, this work identifies an agent application taxonomy, current challenges, and research gaps. In this work, we use a systematic literature review approach. We guide and refine this study and the research questions by applying Population, Intervention, Comparison, Outcome, and Context (PICOC) criteria. The present study investigated approximately 4145 articles involving conversational agents in health published over the last ten years. In this context, we finally selected 40 articles based on their approaches and objectives as related to our main subject. As a result, we developed a taxonomy, identified the main challenges in the field, and defined the main types of dialog and contexts related to conversational agents in health. These results contributed to discussions regarding conversational health agents, and highlighted some research gaps for future study.

Survey of conversational agents in health

In the field of software engineering (SE) the first primary studies about Systematic Literature Review (SLR) have been conducted during the last decade. By adapting guidelines from other disciplines Kitchenham in 2004 created guidelines on performing the SLR in a field of SE. Since then the SLR is used in SE regularly, but many obstacles still remain. This paper aims to give an short but complete overview of all phases and stages that should be undertaken in SLR, and although based on mentioned guidelines, this paper takes into consideration feedback and experiences reported by other authors and discusses the possible approaches along with advantages and dissadvantages of the method.

https://www.bib.irb.hr/595102/download/595102.CECIIS-2012-Performing_systematic_literature_review_in_software_engineering.pdf

Performing systematic literature review in software engineering

Street lighting has a big impact on the energy consumption of Spanish municipalities. To decrease this consumption, the Spanish government has developed two different regulations to improve energy savings and efficiency, and consequently, reduce greenhouse-effect gas emissions. However, after these efforts, they have not obtained the expected results. To improve the effectiveness of these regulations and therefore to optimize energy consumption, a study has been done to analyze the different devices which influence energy consumption with the intention of better understanding their behavior and performance. The devices analyzed were lamps, ballasts, street lamp globes, control systems and dimmable lighting systems. To improve their performance, they have been analyzed from three points of view: changes in technology, use patterns and standards. Thanks to this study, some aspects have been found that could be taken into account if we really wanted to use energy efficiently.

/pdf/a-study-to-improve-the-quality-of-street-lighting-in-spain-4bnwq250qw.pdf

A Study to Improve the Quality of Street Lighting in Spain

Customization is one of the known challenges in traditional ERP systems. With the advent of Cloud ERP systems, a question of determining the state of such systems regarding customization and configuration ability arises. As there are only a few literature sources partially covering this topic, a more comprehensive and systematic literature review is needed. Thus, this paper presents a literature review performed in order to give an overview of reported research on "Cloud ERP Customization" topic performed in the last 5 years. In two search iterations, a total of 32 relevant papers are identified and analyzed. The results show that several dominant research trends are identified along with 12 challenges and issues. Additionally, based on the results, the possible future researches are proposed.

/pdf/cloud-erp-system-customization-challenges-54jhsxwjph.pdf

Cloud ERP System Customization Challenges

Teaching programming faces some general teaching problems, but also confronts some specific problems such as understanding of programming concepts as well as algorithms for solving programming tasks. Our teaching experience with students at university beginner's level has shown that students often try to avoid understanding programming concepts by some "shortcuts", like learning program code by rote, copying programs from colleagues etc. In this paper we introduce automatic on-line generation of programming exercises with code examples for students. This enables high level personalization of student's programming tasks and makes avoiding of understanding concepts more difficult. Along with introduction of the on-line solution, some open questions about correctness of exercises, controlling the solutions and the whole teaching process are also discussed.

Automatic On-Line Generation of Student's Exercises in Teaching Programming

E-learning usage at high-education institutions represents prerequisite of new, modern and quality education. Implementation of suitable learning management system (LMS) is only a first step in realizing this need. Through the last few years LMS Moodle imposed itself as the best solution, and is becoming one of the most common used systems in Croatia. Although it is developed by the „open source“ model that allows quicker and more effective reaction to security bugs inside the LMS itself, vulnerability of the mentioned system greatly depends on the security measures configured on the server. In this paper, we will present a summary of most common security flaws and suggest optimal settings of Moodle LMS and the server itself. Our claims will be supplemented by the results of stress tests and security analysis in order to determine the optimal settings.

/pdf/determination-of-optimal-security-settings-for-lms-moodle-qnst25sjxb.pdf

Zlatko Stapić

Papers

Performing systematic literature review in software engineering

A Study to Improve the Quality of Street Lighting in Spain

Cloud ERP System Customization Challenges

Automatic On-Line Generation of Student's Exercises in Teaching Programming

Determination of optimal security settings for LMS Moodle