2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI)最新文献

Defect Prediction on a Legacy Industrial Software: A Case Study on Software with Few Defects 遗留工业软件的缺陷预测:以少缺陷软件为例

2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI) Pub Date : 2016-05-14 DOI: 10.1145/2896839.2896843

Yavuz Köroglu, A. Sen, Doruk Kutluay, Akin Bayraktar, Yalcin Tosun, Murat Çinar, Hasan Kaya

{"title":"Defect Prediction on a Legacy Industrial Software: A Case Study on Software with Few Defects","authors":"Yavuz Köroglu, A. Sen, Doruk Kutluay, Akin Bayraktar, Yalcin Tosun, Murat Çinar, Hasan Kaya","doi":"10.1145/2896839.2896843","DOIUrl":"https://doi.org/10.1145/2896839.2896843","url":null,"abstract":"Context: Building defect prediction models for software projects is helpful for reducing the effort in locating defects. In this paper, we share our experiences in building a defect prediction model for a large industrial software project. We extract product and process metrics to build models and show that we can build an accurate defect prediction model even when 4% of the software is defective. Objective: Our goal in this project is to integrate a defect predictor into the continuous integration (CI) cycle of a large software project and decrease the effort in testing. Method: We present our approach in the form of an experi- ence report. Specifically, we collected data from seven older versions of the software project and used additional features to predict defects of current versions. We compared several classification techniques including Naive Bayes, Decision Trees, and Random Forest and resampled our training data to present the company with the most accurate defect predictor. Results: Our results indicate that we can focus testing ef- forts by guiding the test team to only 8% of the software where 53% of actual defects can be found. Our model has 90% accuracy. Conclusion: We produce a defect prediction model with high accuracy for a software with defect rate of 4%. Our model uses Random Forest, that which we show has more predictive power than Naive Bayes, Logistic Regression and Decision Trees in our case.","PeriodicalId":386949,"journal":{"name":"2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123915350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Optimizing Software Development Processes 优化软件开发过程

2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI) Pub Date : 2016-05-14 DOI: 10.1145/2896839.2896847

Brendan Murphy

{"title":"Optimizing Software Development Processes","authors":"Brendan Murphy","doi":"10.1145/2896839.2896847","DOIUrl":"https://doi.org/10.1145/2896839.2896847","url":null,"abstract":"It would appear that developing software programs or services is one of the easiest things in the world to do. The theorist would have us believe that all practitioners need to do is to write the code in specific software language (e.g. functional language) and follow specific processes (e.g. agile) and ideally deploy as a continuously evolving service and you will reach perfection. If you run into problems then that is because your engineers are not good enough and/or you are not following the process correctly. The reality is very different, especially when developing software at scale. This talk will describe why there are no universal development processes that can be applied across all software product and service. While practitioners are well aware of this reality, there is little assistance, based on empirical evidence, to help them either choose a suitable development process for their product or service, or in optimizing the solution they have already chosen. Over the last few years I and other researchers have being looking at how to assist developers in optimizing their process, based on the attributes of the product or services they are developing. In this talk I will describe the current state of our research in this space.","PeriodicalId":386949,"journal":{"name":"2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130036669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Evaluating the Benefits of Systematic Project Management in Large Public Sector Projects 评估大型公共部门项目系统项目管理的效益

2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI) Pub Date : 2016-05-14 DOI: 10.1145/2896839.2896841

L. Guzmán, Silke Steinbach, Philipp Diebold, Thomas Zehler, Klaus Schneider, Matthias Habbe

{"title":"Evaluating the Benefits of Systematic Project Management in Large Public Sector Projects","authors":"L. Guzmán, Silke Steinbach, Philipp Diebold, Thomas Zehler, Klaus Schneider, Matthias Habbe","doi":"10.1145/2896839.2896841","DOIUrl":"https://doi.org/10.1145/2896839.2896841","url":null,"abstract":"After an external audit of top-priority projects, the Federal Office for Equipment, Information Technology, and In-Support of the German Armed Forces decided to implement a new project management (PM) system. The new PM system implies major changes in the organization and management of projects and thus affects a large number of stakeholders. We designed a formative evaluation to understand the adequacy and acceptance of the new PM system and to elicit stakeholders’ feedback in order to improve the new PM system and the related transfer strategy prior to rollout. The evaluation also aims at deriving recommendations for increasing the acceptance of the PM system as well as identifying key performance indicators to allow continuous evaluation of the PM system. We chose to perform a multiple-case study in which a sample of stakeholders will try some components of the new PM system and provide feedback on their adequacy. We will also perform interviews and use observations and focus groups to systematically elicit stakeholders’ insights regarding the acceptance of the new PM system and the related transfer strategy.","PeriodicalId":386949,"journal":{"name":"2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134490021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Plug-in Software Engineering Case Studies 插件软件工程案例研究

2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI) Pub Date : 2016-05-14 DOI: 10.1145/2896839.2896840

Tihana Galinac Grbac, P. Runeson

引用次数: 3

Applying Data Analytics towards Optimized Issue Management: An Industrial Case Study 应用数据分析优化问题管理:一个工业案例研究

2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI) Pub Date : 2016-05-14 DOI: 10.1145/2896839.2896845

M. R. Karim, S. Alam, S. Kabeer, G. Ruhe, Basil Baluta, Shafquat Mahmud

{"title":"Applying Data Analytics towards Optimized Issue Management: An Industrial Case Study","authors":"M. R. Karim, S. Alam, S. Kabeer, G. Ruhe, Basil Baluta, Shafquat Mahmud","doi":"10.1145/2896839.2896845","DOIUrl":"https://doi.org/10.1145/2896839.2896845","url":null,"abstract":"This document describes our experience of applying data analytics at Plexina, a leading IT company working in the healthcare domain. The main goal of the project was to identify factors currently affecting issue management and to make analytics based suggestions for optimizing the process. Various statistical and machine learning techniques were applied on a data set extracted from six releases of Plexina, containing more than 666 issues. Statistical techniques successfully identified the various factors that leads to estimation inaccuracy related to issues as well as identified the hidden relationships existing among various variables. The employed predictive analytic models was also successful to some extent, in predicting effort estimation related inaccuracy associated with the issues. The insights provided by the entire data analytics study can be of great help to product managers or the developers to make more informed decisions. In addition, the guidelines presented in this paper based on the lessons learnt can be applied to other data analytics and academia-industry collaboration project.","PeriodicalId":386949,"journal":{"name":"2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132848206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Case Studies in Industry: What We Have Learnt 工业案例研究:我们学到了什么

2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI) Pub Date : 2016-05-14 DOI: 10.1145/2896839.2896844

Daniel Méndez Fernández, S. Wagner

{"title":"Case Studies in Industry: What We Have Learnt","authors":"Daniel Méndez Fernández, S. Wagner","doi":"10.1145/2896839.2896844","DOIUrl":"https://doi.org/10.1145/2896839.2896844","url":null,"abstract":"Case study research has become an important research methodology for exploring phenomena in their natural contexts. Case studies have earned a distinct role in the empirical analysis of software engineering phenomena which are difficult to capture in isolation. Such phenomena often appear in the context of methods and development processes for which it is difficult to run large, controlled experiments as they usually have to reduce the scale in several respects and, hence, are detached from the reality of industrial software development. The other side of the medal is that the realistic socio-economic environments where we conduct case studies – with real-life cases and realistic conditions – also pose a plethora of practical challenges to planning and conducting case studies. In this experience report, we discuss such practical challenges and the lessons we learnt in conducting case studies in industry. Our goal is to help especially inexperienced researchers facing their first case studies in industry by increasing their awareness for typical obstacles they might face and practical ways to deal with those obstacles.","PeriodicalId":386949,"journal":{"name":"2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132500217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Continuous Validation of a Modelling Tool in an Industrial Setting 在工业环境中对建模工具的持续验证

2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI) Pub Date : 2016-05-14 DOI: 10.1145/2896839.2896842

Francisco Valverde, Ó. Pastor

引用次数: 2

Experiences Conducting Experiments in Industry: The ESEIL FiDiPro Project 进行工业实验的经验:ESEIL FiDiPro项目

2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI) Pub Date : 2016-05-14 DOI: 10.1145/2896839.2896846

N. Juristo

{"title":"Experiences Conducting Experiments in Industry: The ESEIL FiDiPro Project","authors":"N. Juristo","doi":"10.1145/2896839.2896846","DOIUrl":"https://doi.org/10.1145/2896839.2896846","url":null,"abstract":"The Experimental Software Engineering Industry Laboratory (ESEIL) project funded by the Finland Distinguished Professor Programme (FiDiPro) kicked off in January 2013. The aim of this research is to gain insight into whether experiments in the software industry can play the role of clinical trials in medicine, that is, field test laboratory findings, acting as the last link in the experimental chain. Besides this research goal, we believe that companies can benefit from the conducted experiments by applying the resulting evidence in their decision-making processes. Controlled experiments in laboratory settings are commonplace in software engineering, but experiments in industry are thin on the ground. Of the few existing cases, most are 1-1 (running one experiment at one company), just a few are n-1 (running n experiments at one company) and still fewer are 1-n (running one and the same experiment at n companies). So far we have conducted the same experiment at seven sites of six companies (four Finnish, one Estonian and one Spanish), and the results have been transferred so that these companies could use the local and global results in decision-making. This talk will discuss several striking and unexpected behaviours regarding both the developers participating in the experiments and the managers receiving and using the results of the experiments. The talk presents challenges and lessons learnt in recruiting participants for experiments, designing and running the experiment, as well as transferring the results.","PeriodicalId":386949,"journal":{"name":"2016 IEEE/ACM 4th International Workshop on Conducting Empirical Studies in Industry (CESI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134497092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2