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This study aimed to identify commit categories in large open source projects that have a high chance of introducing software bugs and to build prediction models to identify risky commits. The researchers categorized commits, extracted metrics like developer experience and lines of code changed, and used those to create models that predicted risky commits with reasonable recall and precision, varying by commit category.
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Capítulo I.
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resume
Fawas Roshan is a sales and marketing associate seeking a position where he can utilize his skills and experience to enhance productivity and reputation. He has over 2 years of experience in retail sales and banking and holds an MBA in Marketing and BCom in Commerce. His contact details and personal information like date of birth, martial status, languages known are also provided.
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A Pecsétviaszgomba
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Capítulo III.
El documento resume la situación actual de un proyecto minero en fase exploratoria en Loma Larga, incluyendo el análisis del terreno, la toma de muestras, y las negociaciones con el gobierno. También describe los beneficios a nivel local por regalías mineras e inversiones en infraestructura, así como los servicios otorgados a la población por parte de las compañías mineras en términos de responsabilidad social.
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This document summarizes Martin Pinzger's research on predicting buggy methods using software repository mining. The key points are:
1. Pinzger and colleagues conducted experiments on 21 Java projects to predict buggy methods using source code and change metrics. Change metrics like authors and method histories performed best with up to 96% accuracy.
2. Predicting buggy methods at a finer granularity than files can save manual inspection and testing effort. Accuracy decreases as fewer methods are predicted but change metrics maintain higher precision.
3. Case studies on two classes show that method-level prediction achieves over 82% precision compared to only 17-42% at the file level. This demonstrates the benefit of finer-
Software Engineering Important Short Question for Exams
The document discusses various topics related to software engineering including:
1. The software development life cycle (SDLC) and its phases like requirements, design, implementation, testing, etc.
2. The waterfall model and its phases from modeling to maintenance.
3. The purpose of feasibility studies, data flow diagrams, and entity relationship diagrams.
4. Different types of testing done during the testing phase like unit, integration, system, black box and white box testing.
Just-in-time Detection of Protection-Impacting Changes on WordPress and Media...
This document presents a study on automatically detecting protection-impacting changes (PICs) in WordPress and MediaWiki source code versions. The study involved analyzing 211 WordPress and 193 MediaWiki release pairs to identify PICs using an existing tool. Characteristics of identified PIC commits were analyzed and machine learning models were developed that achieved high accuracy in predicting PICs. Future work could involve introducing new security metrics to improve models and expanding the research to other systems beyond PHP.
Icse 2011 ds_1
The document discusses pragmatic approaches for prioritizing software quality assurance efforts. It proposes using change and defect metrics at the function level to prioritize unit test creation for legacy code and change risk prediction for new code. The approaches aim to provide focused granularity, responsive feedback on changes, and consideration of effort required. An evaluation of the approaches on open source and commercial projects found the unit test creation heuristic of most frequently modified provided the highest usefulness. The discussion revisits criteria for pragmatic QA approaches.
Populating a Release History Database (ICSM 2013 MIP)
The presentation gives a retrospective, impact, and future research directions of our research on building a release history database from 2003.
A tale of bug prediction in software development
This document discusses using fine-grained source code changes (SCC) to predict bug-prone files in software projects. It presents research that analyzed SCC data from Eclipse projects to predict bugs. The research found that SCC correlated more strongly with bugs than traditional code churn measures, and SCC-based models better predicted bug-prone files and estimated the number of bugs in files compared to models using code churn.
Implementation of reducing features to improve code change based bug predicti...
Abstract Today, we are getting plenty of bugs in the software because of variations in the software and hardware technologies. Bugs are nothing but Software faults, existing a severe challenge for system reliability and dependability. To identify the bugs from the software bug prediction is convenient approach. To visualize the presence of a bug in a source code file, recently, Machine learning classifiers approach is developed. Because of a huge number of machine learned features current classifier-based bug prediction have two major problems i) inadequate precision for practical usage ii) measured prediction time. In this paper we used two techniques first, cos-triage algorithm which have a go to enhance the accuracy and also lower the price of bug prediction and second, feature selection methods which eliminate less significant features. Reducing features get better the quality of knowledge extracted and also boost the speed of computation. Keywords: Efficiency, Bug Prediction, Classification, Feature Selection, Accuracy
J034057065
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Scalable constrained spectral clustering
The document proposes a scalable constrained spectral clustering (SCACS) algorithm to improve the efficiency of existing constrained spectral clustering methods in handling moderate and large datasets. SCACS integrates sparse coding-based graph construction into the constrained normalized cuts framework. This allows it to scale to large datasets while maintaining high clustering accuracy comparable to state-of-the-art methods but with less computational time and side information. The algorithm is presented as the first efficient and scalable version for constrained spectral clustering.
A tale of experiments on bug prediction
Report on two experiment using change and source code metrics to predict bug-prone source files and methods.
Intro-Soft-Engg-2.pptx
The document discusses various software development life cycle models including the waterfall model, iterative waterfall model, prototyping model, evolutionary model, and spiral model. It describes the key phases and advantages/disadvantages of each model. The classical waterfall model is considered theoretical while iterative approaches allow for more flexibility to adapt to changes. Later sections cover requirements analysis, specification techniques like decision trees and tables, modularization principles, and the importance of high cohesion and low coupling in software design.
se02_SW_Process.ppt
The document discusses software process models and activities. It introduces generic process models like waterfall, evolutionary development, and component-based development. It also covers the Rational Unified Process model and its phases. Finally, it discusses various software engineering activities like specification, design, implementation, testing, and evolution as well as the role of computer-aided software engineering tools in supporting software processes.
Software Engineering Process Models
The document discusses various aspects of the software process including software process models, generic process models like waterfall model and evolutionary development, process iteration, and system requirements specification. It provides details on each topic with definitions, characteristics, advantages and diagrams. The key steps in software process are specified as software specifications, design and implementation, validation, and evolution. Generic process models and specific models like waterfall, evolutionary development, and incremental delivery are explained.
software Engineering process
The document discusses software processes and iterative process models. It describes incremental delivery and spiral development as two iterative process models. Incremental delivery breaks development into increments with each delivering part of the functionality. Spiral development represents the process as a spiral with phases addressing objectives, risks, development and planning. Both models allow for iteration and incorporate user feedback earlier.
Survey on Software Defect Prediction (PhD Qualifying Examination Presentation)
This document provides an outline and overview of approaches to software defect prediction. It discusses early approaches using lines of code and complexity metrics from the 1970s-1980s and the development of prediction models using regression and classification in the 1990s-2000s. More recent focus areas discussed include just-in-time prediction models, practical applications of prediction, using history metrics from software repositories, and assessing cross-project prediction feasibility. The document aims to survey the field of software defect prediction.
Using HPC Resources to Exploit Big Data for Code Review Analytics
Code review is one of the crucial software activities where developers and stakeholders collaborate with each other in order to assess software changes. Since code review processes act as a final gate for new software changes to be integrated into the software product, an intense collaboration is necessary in order to prevent defects and produce a high quality of software products. Recently, code review analytics has been implemented in projects (for example, StackAnalytics4 of the OpenStack project) to monitor the collaboration activities between developers and stakeholders in the code review processes. Yet, due to the large volume of software data, code review analytics can only report a static summary (e.g., counting), while neither insights nor instant suggestions are provided. Hence, to better gain valuable insights from software data and help software projects make a better decision, we conduct an empirical investigation using statistical approaches. In particular, we use the large-scale data of 196,712 reviews spread across the Android, Qt, and OpenStack open source projects to train a prediction model in order to uncover the relationship between the characteristics of software changes and the likelihood of having poor code review collaborations. We extract 20 patch characteristics which are grouped along five dimensions, i.e., software changes properties, review participation history, past involvement of a code author, past involvement of reviewers, and review environment dimensions. To validate our findings, we use the bootstrap technique which repeats the experiment 1,000 times. Due to the large volume of studied data, and an intensive computation of characteristic extraction and find- ing validation, the use of the High-Performance-Computing (HPC) re- sources is mandatory to expedite the analysis and generate insights in a timely manner. Through our case study, we find that the amount of review participation in the past and the description length of software changes are a significant indicator that new software changes will suffer from poor code review collaborations [2017]. Moreover, we find that the purpose of introducing new features can increase the likelihood that new software changes will receive late collaboration from reviewers. Our findings highlight the need for the policies of software change submission that monitor these characteristics in order to help software projects improve the quality of code reviews processes. Moreover, based on our findings, future work should develop real-time code review analytics implemented on HPC resources in order to instantly provide insights and suggestions to software projects
Esem2010 shihab
This study examines the impact of code and process metrics on post-release defects in Eclipse 2.0, 2.1 and 3.0. It finds that a small set of just 3-4 metrics including prior defects, size, changes and complexity can predict post-release defects comparably to models using all 34 metrics. The most important metrics are size and prior defects. Odds ratios show their impact decreases defects, with size having the greatest effect. Simple models outperform 95% PCA models using fewer metrics.
Searching for Quality: Genetic Algorithms and Metamorphic Testing for Softwar...
More machine learning (ML) models are introduced to the field of Software Engineering (SE) and reached a stage of maturity to be considered for real-world use; But the real world is complex, and testing these models lacks often in explainability, feasibility and computational capacities. Existing research introduced meta-morphic testing to gain additional insights and certainty about the model, by applying semantic-preserving changes to input-data while observing model-output. As this is currently done at random places, it can lead to potentially unrealistic datapoints and high computational costs. With this work, we introduce genetic search as an aid for metamorphic testing in SE ML. Exploiting the delta in output as a fitness function, the evolutionary intelligence optimizes the transformations to produce higher deltas with less changes. We perform a case study minimizing F1 and MRR for Code2Vec on a representative sample from java-small with both genetic and random search. Our results show that within the same amount of time, genetic search was able to achieve a decrease of 10% in F1 while random search produced 3% drop.
Soft Eng - Software Process
The document introduces software process models including the waterfall model, evolutionary development, and component-based software engineering. It describes the Rational Unified Process model and discusses key process activities like requirements engineering, design, implementation, testing, and evolution. Computer-aided software engineering tools are introduced as a way to support various activities in the software development process.
Ch4
The document introduces software process models and describes three generic models: waterfall, evolutionary development, and component-based development. It also outlines the software development process including requirements engineering, design, implementation, testing, and evolution. The Rational Unified Process model is introduced as a modern iterative process model. Computer-aided software engineering tools are discussed as a way to support software process activities.
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Software Engineering Important Short Question for Exams
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Just-in-time Detection of Protection-Impacting Changes on WordPress and Media...
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Populating a Release History Database (ICSM 2013 MIP)
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Survey on Software Defect Prediction (PhD Qualifying Examination Presentation)
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poster_3.0
- 1. Studying Bug-Introducing Commits in Large Open Source Projects Stefano Sansone Dr. Emad Shihab Dept. of Software Engineering Software systems are built from many small changes, called software commits. In general, the goal of these commits is to fix a prior error (i.e., software bug) or to enhance the functionality of the software system. However, in some cases, commits themselves can introduce software bugs. These commits can be very costly and in our research we focus on effectively identifying software commits that have a high chance of introducing bugs. Results Approach Background RQ 1: Do certain categories of commits tend to be risky? RQ 2: Can we accurately predict risky changes? Does the prediction accuracy differ for the various categories? RQ 3: How many and what metrics do we need to accurately predict risky changes? Do the metrics and the number of metrics differ for the various categories? Motivation Metrics CHURN – No. of lines added, deleted and modified ADD – No. of lines added DEL – No. of lines deleted NS – No. of modified subsystems ND – No. of modified directories NF – No. of modified files Ent – distribution of modified code across each file NDEV – No. of developers that changed the modified files AGE – average time interval between the last and current change NFC – No. of unique changes to the modified files EXP – developer experience REXP – recent developer experience SEXP – developer experience on a subsystem Largest risky categories • Corrective • Feature Addition • Non Functional • Good Recall • Precision could be better Most used metrics • Developer Experience • Lines Modified • No. Subsystems • No. Developers Number of Metrics • Balance of recall and precision with 7 metrics • Tradeoff between Precision and Recall • Precision slightly higher for Corrective category (similar to other categories) Categorize the commits by their commit messages. Extract metrics from the commits. Create two prediction models for each category to identify risky commits. A generalized linear model (GLM) and a model that compares the median metric values of risky changes. Bug-introducing (“risky”) commits is any commit that is later changed by another commit (the fix commit). These risky changes are found by identifying the commits that first introduced a line of code that needed to be changed. Our research attempts to develop prediction models to be able to predict if a change is risky as soon as it is added to the repository. Previous work has focused on predicting bug-introducing (risky) changes by using a set of metrics and building prediction models off of them. Our work studies large open source projects and tries to find categories of commits that have a high percentage of risky commits. Using this separation of commits into categories, we build prediction models based on the metrics of individual categories to predict risky changes. Metric All Corrective Feature Addition Non Functional Perfective Preventive Developer Experience 84 62 70 48 26 46 Lines Modified 82 98 80 74 38 66 No. Subsystems 80 58 70 28 46 32 No. Developers 64 54 60 20 10 52 Developer Experience on a subsystem 56 72 36 42 20 44 Distribution of Modified Code 50 24 44 6 18 8 No. Unique Changes 44 34 42 24 10 6 Time since last change 36 6 22 20 32 24 Lines Added 20 20 0 40 0 8 No. Directories 20 40 18 26 12 38 No. Files 20 8 4 12 16 0 Lines Deleted 0 16 0 0 0 0 Recent Developer Experience 0 0 0 0 0 0 Metric Usage Percent by Category In GLM Prediction Model Project Risky Ratio Recall Precision F-measure Maven-2 0.235 0.5 0.336 0.403 Perl 0.289 0.5 0.141 0.221 PostgreSQL 0.428 0.582 0.446 0.506 Rails 0.269 0.619 0.111 0.188 Precision - fraction of retrieved instances that are relevant Recall - fraction of relevant instances that are retrieved F-measure - weighted harmonic mean of precision and recall Extract Data Categorize Extract Metrics Compare Metrics and Identify Risky Commits Corrective Feature Addition Non Functional Perfective Preventive Project Maven-2 Perl PostgreSQL Rails Language Java C C Ruby Start Date Sept '03 Dec '87 Jul '96 Nov '04 End Date May '12 Jun '13 Jun '13 Jun '13 No. Commits 5638 51107 35194 37955 No. Developers 34 1181 38 2342 Domain build automation tool interpreted, dynamic programmi ng language object-relational database management system web application framework Extract commit data from four large open source projects.