Retrieval from software libraries for bug localization

2021 ◽  
Vol 46 (3) ◽  
pp. 33-36
Author(s):  
Shivani Rao ◽  
Avinash Kak

This retrospective on our 2011 MSR publication starts with the research milieu that led to the work reported in our paper. We brie y review the competing ideas of a decade ago that could be applied to solving the problem of identifying the les in a software library related to a query. We were especially interested in nding out if the more complex text retrieval methods of that time would be e ective in the software context. A surprising conclusion of our paper was that the reality was exactly the opposite: the more traditional simpler methods outperformed the complex methods. In addition to this surprising result, our paper was also the rst to report what was considered at that time a large-scale quantitative evaluation of the IR-based approaches to automatic bug localization. Over the years, such quantitative evaluations have become the norm. We believe that these contributions were largely responsible for the popularity of this paper in the research literature.

mSphere ◽  
2017 ◽  
Vol 2 (5) ◽  
Author(s):  
Gaorui Bian ◽  
Gregory B. Gloor ◽  
Aihua Gong ◽  
Changsheng Jia ◽  
Wei Zhang ◽  
...  

ABSTRACT We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations. The microbiota of the aged is variously described as being more or less diverse than that of younger cohorts, but the comparison groups used and the definitions of the aged population differ between experiments. The differences are often described by null hypothesis statistical tests, which are notoriously irreproducible when dealing with large multivariate samples. We collected and examined the gut microbiota of a cross-sectional cohort of more than 1,000 very healthy Chinese individuals who spanned ages from 3 to over 100 years. The analysis of 16S rRNA gene sequencing results used a compositional data analysis paradigm coupled with measures of effect size, where ordination, differential abundance, and correlation can be explored and analyzed in a unified and reproducible framework. Our analysis showed several surprising results compared to other cohorts. First, the overall microbiota composition of the healthy aged group was similar to that of people decades younger. Second, the major differences between groups in the gut microbiota profiles were found before age 20. Third, the gut microbiota differed little between individuals from the ages of 30 to >100. Fourth, the gut microbiota of males appeared to be more variable than that of females. Taken together, the present findings suggest that the microbiota of the healthy aged in this cross-sectional study differ little from that of the healthy young in the same population, although the minor variations that do exist depend upon the comparison cohort. IMPORTANCE We report the large-scale use of compositional data analysis to establish a baseline microbiota composition in an extremely healthy cohort of the Chinese population. This baseline will serve for comparison for future cohorts with chronic or acute disease. In addition to the expected difference in the microbiota of children and adults, we found that the microbiota of the elderly in this population was similar in almost all respects to that of healthy people in the same population who are scores of years younger. We speculate that this similarity is a consequence of an active healthy lifestyle and diet, although cause and effect cannot be ascribed in this (or any other) cross-sectional design. One surprising result was that the gut microbiota of persons in their 20s was distinct from those of other age cohorts, and this result was replicated, suggesting that it is a reproducible finding and distinct from those of other populations.


1992 ◽  
Vol 70 (1) ◽  
pp. 89-95 ◽  
Author(s):  
Stefan Andersson

A 3-year demographic study was conducted to reveal targets of selection on morphology and life history in a population of Crepis tectorum ssp. pumila, a winter annual plant confined to calcareous grasslands (alvars) on the Baltic island of Öland (south Sweden). I calculated the selection differential to describe the change in the mean value of a character due to selection and used multiple regression analyses to partition the direct effect of selection on the trait from indirect responses of selection on other traits. Rosette leaf number, a convenient measure of plant size, was strongly correlated with both viability and fertility (fitness). There was also a strong relationship between fitness and the extent to which the plants expressed traits characterizing this particular taxon. Multiple regression analyses indicated direct selection favouring plants with deeply lobed leaves and a densely branched stem, two distinctive traits of ssp. pumila believed to be adaptive in the alvar habitat. Only stem height was subject to both direct and indirect selection in the wrong direction; taller individuals were more successful than those with a shorter stem, a surprising result considering the inferred advantage of a short stature in the exposed alvar habitat. Selection on other traits assumed to be ecologically important (germination time, flowering time, and seed size) was found to be either absent or variable in direction when other traits were held constant. The failure of plants to survive to the flowering stage in the last two summers indicates strong selection for plants that produce a high percentage of dormant seeds. Overall, the contemporary selection regime as revealed by demographic data was only partly congruent with predictions regarding historical selection pressures based on large-scale patterns of variation (ecotypic differentiation). Key words: Crepis tectorum, ecotypic differentiation, life history, morphology, phenotypic selection.


Author(s):  
Yu Zhou ◽  
Yanxiang Tong ◽  
Taolue Chen ◽  
Jin Han

Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source-file localization approach, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of the approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e. ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat.


Author(s):  
George Hripcsak ◽  
Martijn J. Schuemie ◽  
David Madigan ◽  
Patrick B. Ryan ◽  
Marc A. Suchard

Summary Objective: The current observational research literature shows extensive publication bias and contradiction. The Observational Health Data Sciences and Informatics (OHDSI) initiative seeks to improve research reproducibility through open science. Methods: OHDSI has created an international federated data source of electronic health records and administrative claims that covers nearly 10% of the world’s population. Using a common data model with a practical schema and extensive vocabulary mappings, data from around the world follow the identical format. OHDSI’s research methods emphasize reproducibility, with a large-scale approach to addressing confounding using propensity score adjustment with extensive diagnostics; negative and positive control hypotheses to test for residual systematic error; a variety of data sources to assess consistency and generalizability; a completely open approach including protocol, software, models, parameters, and raw results so that studies can be externally verified; and the study of many hypotheses in parallel so that the operating characteristics of the methods can be assessed. Results: OHDSI has already produced findings in areas like hypertension treatment that are being incorporated into practice, and it has produced rigorous studies of COVID-19 that have aided government agencies in their treatment decisions, that have characterized the disease extensively, that have estimated the comparative effects of treatments, and that the predict likelihood of advancing to serious complications. Conclusions: OHDSI practices open science and incorporates a series of methods to address reproducibility. It has produced important results in several areas, including hypertension therapy and COVID-19 research.


2020 ◽  
Author(s):  
Albert A Gayle

Year-to-year emergence of West Nile virus has been sporadic and notoriously hard to predict. In Europe, 2018 saw a dramatic increase in the number of cases and locations affected. In this work, we demonstrate a novel method for predicting outbreaks and understanding what drives them. This method creates a simple model for each region that directly explains how each variable affects risk. Behind the scenes, each local explanation model is produced by a state-of-the-art AI engine. This engine unpacks and restructures output from an XGBoost machine learning ensemble. XGBoost, well-known for its predictive accuracy, has always been considered a "black box" system. Not any more. With only minimal data curation and no "tuning", our model predicted where the 2018 outbreak would occur with an AUC of 97%. This model was trained using data from 2010-2016 that reflected many domains of knowledge. Climate, sociodemographic, economic, and biodiversity data were all included. Our model furthermore explained the specific drivers of the 2018 outbreak for each affected region. These effect predictions were found to be consistent with the research literature in terms of priority, direction, magnitude, and size of effect. Aggregation and statistical analysis of local effects revealed strong cross-scale interactions. From this, we concluded that the 2018 outbreak was driven by large-scale climatic anomalies enhancing the local effect of mosquito vectors. We also identified substantial areas across Europe at risk for sudden outbreak, similar to that experienced in 2018. Taken as a whole, these findings highlight the role of climate in the emergence and transmission of West Nile virus. Furthermore, they demonstrate the crucial role that the emerging "eXplainable AI" (XAI) paradigm will have in predicting and controlling disease.


2009 ◽  
Vol 23 (4) ◽  
pp. 211-230 ◽  
Author(s):  
Jonah Rockoff

A vast majority of adults believe that class size reductions are a good way to improve the quality of public schools. Reviews of the research literature, on the other hand, have provided mixed messages on the degree to which class size matters for student achievement. Here I will discuss a substantial, but overlooked, body of experimental work on class size that developed prior to World War II. These field experiments did not have the benefit of modern econometrics, and only a few were done on a reasonably large scale. However, they often used careful empirical designs, and the collective magnitude of this body of work is considerable. Moreover, this research produced little evidence to suggest that students learn more in smaller classes, which stands in contrast to some, though not all, of the most recent work by economists. In this essay, I provide an overview of the scope and breadth of the field experiments in class size conducted prior to World War II, the motivations behind them, and how their experimental designs were crafted to deal with perceived sources of bias. I discuss how one might interpret the findings of these early experimental results alongside more recent research.


2021 ◽  
Author(s):  
Thi Mai Anh Bui ◽  
Nhat Hai Nguyen

Precisely locating buggy files for a given bug report is a cumbersome and time-consuming task, particularly in a large-scale project with thousands of source files and bug reports. An efficient bug localization module is desirable to improve the productivity of the software maintenance phase. Many previous approaches rank source files according to their relevance to a given bug report based on simple lexical matching scores. However, the lexical mismatches between natural language expressions used to describe bug reports and technical terms of software source code might reduce the bug localization system’s accuracy. Incorporating domain knowledge through some features such as the semantic similarity, the fixing frequency of a source file, the code change history and similar bug reports is crucial to efficiently locating buggy files. In this paper, we propose a bug localization model, BugLocGA that leverages both lexical and semantic information as well as explores the relation between a bug report and a source file through some domain features. Given a bug report, we calculate the ranking score with every source files through a weighted sum of all features, where the weights are trained through a genetic algorithm with the aim of maximizing the performance of the bug localization model using two evaluation metrics: mean reciprocal rank (MRR) and mean average precision (MAP). The empirical results conducted on some widely-used open source software projects have showed that our model outperformed some state of the art approaches by effectively recommending relevant files where the bug should be fixed.


2001 ◽  
Vol 178 (S41) ◽  
pp. s191-s194 ◽  
Author(s):  
John Geddes ◽  
Guy Goodwin

BackgroundThe increasing use of the methods of evidence-based medicine to keep up-to-date with the research literature highlights the absence of high-quality evidence in many areas in psychiatry.AimsTo outline current uncertainties in the maintenance treatment of bipolar disorder and to describe some of the decisions involved in designing a large simple trial.MethodWe describe some of the strategies of evidence-based medicine, and how they can be applied in practice, focusing specifically on the area of bipolar disorder.ResultsOne of the key clinical uncertainties in the treatment of bipolar disorder is the place of maintenance drug treatments and their relative efficacy. A large-scale study, the Bipolar Affective Disorder: Lithium Anticonvulsant Evaluation (BALANCE) trial, is proposed to compare the effectiveness of lithium, valproate and the combination of lithium and valproate.ConclusionsProviding reliable answers to key clinical questions in psychiatry will require new approaches to clinical trials. These will need to be far larger than previously appreciated and will therefore need to be collaborative ventures involving front-line clinicians.


2011 ◽  
Vol 70 (3) ◽  
pp. 165-174 ◽  
Author(s):  
Rebecca Brauchli ◽  
Georg F. Bauer ◽  
Oliver Hämmig

This study investigates whether and how time-based work-to-life conflict and time-based life-to-work conflict are related to burnout among Swiss employees. The sample consisted of 6,091 female and male employees of four large-scale Swiss enterprises in various economic sectors (healthcare, banking, insurance, and logistics) and occupational groups (bankers, insurance company employees, nurses, physicians, technical staff, administrative staff, baggage handlers, etc.). Participants completed an extensive questionnaire relating to specific work and general life conditions, the integration of work and private life, health, and socioeconomic factors. Employees reported greater time-based work-to-life conflict than time-based life-to-work conflict. Regression analyses indicated that time-based work-to-life conflict appeared to be strongly associated with burnout. Time-based life-to-work conflict seems to be a weaker, but equally significant predictor of burnout. The present study contributes to the research literature on work-life conflict in Switzerland and its association with burnout. Since the results suggest a strong association between time-based work-to-life conflict and burnout, intervention strategies that help to mitigate such conflict may also reduce burnout.


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