Proceedings of the 9th International Conference on Predictive Models in Software Engineering最新文献

{"title":"Using evidential reasoning to make qualified predictions of software quality","authors":"Neil Walkinshaw","doi":"10.1145/2499393.2499402","DOIUrl":"https://doi.org/10.1145/2499393.2499402","url":null,"abstract":"Software quality is commonly characterised in a top-down manner. High-level notions such as quality are decomposed into hierarchies of sub-factors, ranging from abstract notions such as maintainability and reliability to lower-level notions such as test coverage or team-size. Assessments of abstract factors are derived from relevant sources of information about their respective lower-level sub-factors, by surveying sources such as metrics data and inspection reports. This can be difficult because (1) evidence might not be available, (2) interpretations of the data with respect to certain quality factors may be subject to doubt and intuition, and (3) there is no straightforward means of blending hierarchies of heterogeneous data into a single coherent and quantitative prediction of quality. This paper shows how Evidential Reasoning (ER) - a mathematical technique for reasoning about uncertainty and evidence - can address this problem. It enables the quality assessment to proceed in a bottom-up manner, by the provision of low-level assessments that make any uncertainty explicit, and automatically propagating these up to higher-level 'belief-functions' that accurately summarise the developer's opinion and make explicit any doubt or ignorance.","PeriodicalId":260705,"journal":{"name":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121229863","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}

{"title":"The impact of parameter tuning on software effort estimation using learning machines","authors":"Liyan Song, Leandro L. Minku, X. Yao","doi":"10.1145/2499393.2499394","DOIUrl":"https://doi.org/10.1145/2499393.2499394","url":null,"abstract":"Background: The use of machine learning approaches for software effort estimation (SEE) has been studied for more than a decade. Most studies performed comparisons of different learning machines on a number of data sets. However, most learning machines have more than one parameter that needs to be tuned, and it is unknown to what extent parameter settings may affect their performance in SEE. Many works seem to make an implicit assumption that parameter settings would not change the outcomes significantly. Aims: To investigate to what extent parameter settings affect the performance of learning machines in SEE, and what learning machines are more sensitive to their parameters. Method: Considering an online learning scenario where learning machines are updated with new projects as they become available, systematic experiments were performed using five learning machines under several different parameter settings on three data sets. Results: While some learning machines such as bagging using regression trees were not so sensitive to parameter settings, others such as multilayer perceptrons were affected dramatically. Combining learning machines into bagging ensembles helped making them more robust against different parameter settings. The average performance of k-NN across different projects was not so much affected by different parameter settings, but the parameter settings that obtained the best average performance across time steps were not so consistently the best throughout time steps as in the other approaches. Conclusions: Learning machines that are more/less sensitive to different parameter settings were identified. The different sensitivity obtained by different learning machines shows that sensitivity to parameters should be considered as one of the criteria for evaluation of SEE approaches. A good learning machine for SEE is not only one which is able to achieve superior performance, but also one that is either less dependent on parameter settings or to which good parameter choices are easy to make.","PeriodicalId":260705,"journal":{"name":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133761483","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}

{"title":"Building a second opinion: learning cross-company data","authors":"Ekrem Kocaguneli, B. Cukic, T. Menzies, Huihua Lu","doi":"10.1145/2499393.2499400","DOIUrl":"https://doi.org/10.1145/2499393.2499400","url":null,"abstract":"Background: Developing and maintaining a software effort estimation (SEE) data set within a company (within data) is costly. Often times parts of data may be missing or too difficult to collect, e.g. effort values. However, information about the past projects-although incomplete- may be helpful, when incorporated with the SEE data sets from other companies (cross data). Aim: Utilizing cross data to aid within company estimates and local experts; Proposing a synergy between semi-supervised, active and cross company learning for software effort estimation. Method: The proposed method: 1) Summarizes existing unlabeled within data; 2) Uses cross data to provide pseudo-labels for the summarized within data; 3) Uses steps 1 and 2 to provide an estimate for the within test data as an input for the local company experts. We use 21 data sets and compare the proposed method to existing state-of-the-art within and cross company effort estimation methods subject to evaluation by 7 different error measures. Results: In 132 out of 147 settings (21 data sets X 7 error measures = 147 settings), the proposed method performs as well as the state-of-the-art methods. Also, the proposed method summarizes the past within data down to at most 15% of the original data. Conclusion: It is important to look for synergies amongst cross company and within-company effort estimation data, even when the latter is imperfect or sparse. In this research, we provide the experts with a method that: 1) is competent (performs as well as prior within and cross data estimation methods) 2) reflects on local data (estimates come from the within data); 3) is succinct (summarizes within data down to 15% or less); 4) cheap (easy to build).","PeriodicalId":260705,"journal":{"name":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114409613","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}

{"title":"Training data selection for cross-project defect prediction","authors":"S. Herbold","doi":"10.1145/2499393.2499395","DOIUrl":"https://doi.org/10.1145/2499393.2499395","url":null,"abstract":"Software defect prediction has been a popular research topic in recent years and is considered as a means for the optimization of quality assurance activities. Defect prediction can be done in a within-project or a cross-project scenario. The within-project scenario produces results with a very high quality, but requires historic data of the project, which is often not available. For the cross-project prediction, the data availability is not an issue as data from other projects is readily available, e.g., in repositories like PROMISE. However, the quality of the defect prediction results is too low for practical use. Recent research showed that the selection of appropriate training data can improve the quality of cross-project defect predictions. In this paper, we propose distance-based strategies for the selection of training data based on distributional characteristics of the available data. We evaluate the proposed strategies in a large case study with 44 data sets obtained from 14 open source projects. Our results show that our training data selection strategy improves the achieved success rate of cross-project defect predictions significantly. However, the quality of the results still cannot compete with within-project defect prediction.","PeriodicalId":260705,"journal":{"name":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116561117","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}

{"title":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","authors":"Burak Turhan","doi":"10.1145/2499393","DOIUrl":"https://doi.org/10.1145/2499393","url":null,"abstract":"","PeriodicalId":260705,"journal":{"name":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131526539","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}

{"title":"Beyond data mining; towards \"idea engineering\"","authors":"T. Menzies","doi":"10.1145/2499393.2499401","DOIUrl":"https://doi.org/10.1145/2499393.2499401","url":null,"abstract":"Pablo Picasso said \"computers are stupid- they only give you answers\". I seek to build reasoners that are not stupid- that know predictions and decisions are important, but so too are the questions and insights generated on the way to those conclusions. Within a society of carbon and/or silicon-based agents, discussion systems let us share, reflect, and try to improve each other's insights.","PeriodicalId":260705,"journal":{"name":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122400577","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}

{"title":"Incremental development productivity decline","authors":"Ramin Moazeni, Daniel Link, B. Boehm","doi":"10.1145/2499393.2499403","DOIUrl":"https://doi.org/10.1145/2499393.2499403","url":null,"abstract":"Incremental models are now being used by many organizations in order to reduce development risks while trying to deliver the product on time. It has become the most common method of software development with characteristics that influences the productivity of projects. This paper introduces a phenomenon called Incremental Development Productivity Decline (IDPD) that is presumed to be present in all incremental software projects to some extent. Different ways of measuring productivity are presented and evaluated in order to come to a definition or set of definitions that is suitable to these kinds of projects. Based on their coherence and other common characteristics, incrementally developed projects are split into several major categories. Following this, several major projects are used as case studies in order to find out whether IDPD can be proven to exist.","PeriodicalId":260705,"journal":{"name":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114679252","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}

{"title":"Software analytics = sharing information","authors":"Thomas Zimmermann","doi":"10.1145/2499393.2499405","DOIUrl":"https://doi.org/10.1145/2499393.2499405","url":null,"abstract":"Software and its development generates an inordinate amount of data. Development activities such as check-ins, work items, bug reports, code reviews, and test executions are recorded in software repositories. User interactions that reflect how customers experience software are recorded in telemetry data, run-time traces, and log files and helps to track application and feature usage and expose performance and reliability. Software analytics takes this data and turns it into actionable insight to better inform decisions related to software. To a large extent, software analytics is about what we can learn and share about software. This include our own projects but also projects by others. Looking back at decades of research in empirical software engineering and mining software repositories, software analytics lets us share different things: insights, models, methods, and data. In this talk, I will present successful efforts from Microsoft and academia on software analytics and try to convince you that analytics is all about sharing information.","PeriodicalId":260705,"journal":{"name":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","volume":"14 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126926422","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}

{"title":"A comparative evaluation of static analysis actionable alert identification techniques","authors":"S. Heckman, L. Williams","doi":"10.1145/2499393.2499399","DOIUrl":"https://doi.org/10.1145/2499393.2499399","url":null,"abstract":"Automated static analysis (ASA) tools can identify potential source code anomalies that could lead to field failures. Developer inspection is required to determine if an ASA alert is important enough to fix, or an actionable alert. Supplementing current ASA tools with automated identification of actionable alerts could reduce developer inspection overhead, leading to an increase in industry adoption of ASA tools. The goal of this research is to inform the selection of an actionable alert identification technique for ranking the output of automated static analysis through a comparative evaluation of actionable alert identification techniques. We investigated six actionable alert identification techniques on three subject projects. Among these six techniques, the systematic actionable alert identification (SAAI) technique reported an average accuracy of 82.5% across the three subject projects when considering both ASA tools evaluated. Check 'n' Crash reported an average accuracy of 85.8% for the single ASA tool evaluated. The other actionable alert identification techniques had average accuracies ranging from 42.2%-78.2%.","PeriodicalId":260705,"journal":{"name":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131399702","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}

{"title":"An algorithmic approach to missing data problem in modeling human aspects in software development","authors":"G. Çalikli, A. Bener","doi":"10.1145/2499393.2499398","DOIUrl":"https://doi.org/10.1145/2499393.2499398","url":null,"abstract":"Background: In our previous research, we built defect prediction models by using confirmation bias metrics. Due to confirmation bias developers tend to perform unit tests to make their programs run rather than breaking their code. This, in turn, leads to an increase in defect density. The performance of prediction model that is built using confirmation bias was as good as the models that were built with static code or churn metrics. Aims: Collection of confirmation bias metrics may result in partially \"missing data\" due to developers' tight schedules, evaluation apprehension and lack of motivation as well as staff turnover. In this paper, we employ Expectation-Maximization (EM) algorithm to impute missing confirmation bias data. Method: We used four datasets from two large-scale companies. For each dataset, we generated all possible missing data configurations and then employed Roweis' EM algorithm to impute missing data. We built defect prediction models using the imputed data. We compared the performances of our proposed models with the ones that used complete data. Results: In all datasets, when missing data percentage is less than or equal to 50% on average, our proposed model that used imputed data yielded performance results that are comparable with the performance results of the models that used complete data. Conclusions: We may encounter the \"missing data\" problem in building defect prediction models. Our results in this study showed that instead of discarding missing or noisy data, in our case confirmation bias metrics, we can use effective techniques such as EM based imputation to overcome this problem.","PeriodicalId":260705,"journal":{"name":"Proceedings of the 9th International Conference on Predictive Models in Software Engineering","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132932364","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}