Ayse Basar

Toronto Metropolitan (Ryerson) University · Mechanical and Industrial Engineering

Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software development processes by automatically identifying conflicting and duplicate software requirement specifications. We formulate the conflict and duplicate detection problem as...

Purpose: To date there has not been an extensive analysis of the outcomes of biomarker use in oncology. Methods: Data were pooled across four indications in oncology drawing upon trial outcomes from www.clinicaltrials.gov: breast cancer, non-small cell lung cancer (NSCLC), melanoma and colorectal cancer from 1998 to 2017. We compared the likelihoo...

Background: Although the appearance of the brain shows one unique organism, the brain is composed of cognitive regions. Each region can represent some specific function in the brain. The study of the brain’s morphology is important to identify changes in the regions and the segmentation process can classify those regions. Several studies conducted...

Background: Developers spend a significant amount of time and efforts to localize bugs. In the literature, many researchers proposed state-of-the-art bug localization models to help developers localize bugs easily. The practitioners, on the other hand, expect a bug localization tool to meet certain criteria, such as trustworthiness, scalability, an...

The main goal of this paper is to determine the best feature selection algorithm to use on large biomedical datasets. Feature Selection shows a potential role in analyzing large biomedical datasets. Four different feature selection techniques have been employed on large biomedical datasets. These techniques were Information Gain, Chi-Squared, Marko...

Aim: The paper aims to automatically generate commentaries on financial reports. Background: Analysing and commenting financial reports is critical to evaluate the performance of a company so that management may change course to meet the targets. Generating commentaries is a task that relies on the expertise of analysts. Methodology: We propose an...

Verbal autopsy (VA) deals with post-mortem surveys about deaths, mostly in low and middle income countries, where the majority of deaths occur at home rather than a hospital, for retrospective assignment of causes of death (COD) and subsequently evidence-based health system strengthening. Automated algorithms for VA COD assignment have been develop...

Verbal autopsy (VA) deals with post-mortem surveys about deaths, mostly in low and middle income countries, where the majority of deaths occur at home rather than a hospital, for retrospective assignment of causes of death (COD) and subsequently evidence-based health system strengthening. Automated algorithms for VA COD assignment have been develop...

Clinical applications of electroencephalography (EEG) span a very broad range of diagnostic conditions. Epileptic seizure is the fourth most common neurological disorder in that. There has been considerable progress in clinical understanding of epilepsy, however many aspects of seizure prevention are still a mystery. Predictive modeling of EEG can...

Aim: In this study, we aim to re-evaluate research questions on the ability of a logistic regression model proposed in a previous work to predict and prioritize the failing test cases based on some test quality metrics. Background: The process of prioritizing test cases aims to come up with a ranked test suite where test cases meeting certain crite...

Although software does not consume energy by itself, its characteristics determine which hardware resources are made available and how much energy is used. Therefore, energy efficiency of software products has become a popular agenda for both industry and academia in recent years. Designing such software is now a core initiative of software develop...

Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have draw...

Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have draw...

Bayesian networks (BN) have been used for decision making in software engineering for many years. In other fields such as bioinformatics, BNs are rigorously evaluated in terms of the techniques that are used to build the network structure and to learn the parameters. We extend our prior mapping study to investigate the extent to which contextual an...

Evolutionary coupling (EC) is defined as the implicit relationship between 2 or more software artifacts that are frequently changed together. Changing software is widely reported to be defect-prone. In this study, we investigate the effect of EC on the defect proneness of large industrial software systems and explain why the effects vary. We analys...

Background: On projects with tight schedules and limited budgets, it may not be possible to resolve all known bugs before the next release. Estimates of the time required to fix known bugs (the "bug fixing time") would assist managers in allocating bug fixing resources when faced with a high volume of bug reports. Aim: In this work, we aim to repli...

For more than two decades, we have witnessed how software is developed in the industry and later conducted empirical research in academia to build predictive models, mine software repositories, and perform various software analytics tasks to help software development teams make evidence-based decisions.

We conduct a replication study to define temporal patterns of activity sequences in a proprietary dataset, and compare them with an open-source dataset. Temporal bug repository data may give many insights in the context of root-cause analysis of defects. Observing activities based on temporal changes enables the formation of temporal activity seque...

Context: Software code review, as an engineering best practice, refers to the inspection of the code change in order to find possible defects and ensure change quality. Code reviews, however, may not guarantee finding the defects. Thus, there is a risk for a defective code change in a given patch, to pass the review process and be submitted. Goal:...

Request for Proposal (RFP) is a solicitation of proposals from vendors and they are often judged by human experts from varying backgrounds and experiences. This is typically done because large technical RFPs require a diverse group of evaluators who will bring their skills and experience to bear. However, different people with different backgrounds...

Zero-day vulnerabilities continue to be a threat as they are unknown to vendors; when attacks occur, vendors have zero days to provide remedies. New techniques for the detection of zero-day vulnerabilities on software systems are being developed but they have their own limitations; e.g., anomaly detection techniques are prone to false alarms. To be...

In software engineering, the primary objective is delivering high-quality systems within budget and time constraints. Managers struggle to make many decisions under a lot of uncertainty. They would like to be confident in the product, team, and the processes. Therefore, the need for evidence-based decision making, a.k.a. data science and analysis,...

In this chapter, we share our experience and views on software data analytics in practice with a review of our previous work. In more than 10 years of joint research projects with industry, we have encountered similar data analytics patterns in diverse organizations and in different problem cases. We discuss these patterns following a "software ana...

Confirmation bias is defined as the tendency of people to seek evidence that verifies a hypothesis rather than seeking evidence to falsify it. Due to the confirmation bias, defects may be introduced in a software product during requirements analysis, design, implementation and/or testing phases. For instance, testers may exhibit confirmatory behavi...

Recently, sustainability in software engineering and especially in requirements engineering is an emerging field. Especially, increasing demand for energy and intensive use of software and software-related services are the key motivators for designing software products with environmental requirements. In this study, we identify the software practit...

Defect prediction models presented in the literature lack generalization unless the original study can be replicated using new datasets and in different organizational settings. Practitioners can also benefit from replicating studies in their own environment by gaining insights and comparing their findings with those reported. In this work, we repl...

The bug tracking repositories of software projects capture initial defect (bug) reports and the history of interactions among developers, testers, and customers. Extracting and mining information from these repositories is time consuming and daunting. Researchers have focused mostly on analyzing the frequency of the occurrence of defects and their...

In this chapter, we share our experience and views on software data analytics in practice with a retrospect to our previous work. Over ten years of joint research projects with the industry, we have encountered similar data analytics patterns in diverse organizations and in different problem cases. We discuss these patterns following a 'software an...

Context: Number of defects fixed in a given month is used as an input for several project management decisions such as release time, maintenance effort estimation and software quality assessment. Past activity of developers and testers may help us understand the future number of reported defects. Goal: To find a simple and easy to implement solutio...

Evolutionary coupling is defined as the implicit relationship between two or more software artifacts that are frequently changed together. In this study we investigate the effect of evolutionary coupling on defect proneness of a large financial legacy software in an industrial software development environment. We collected historical data for 5 yea...

Green software development is a relatively new research area within green IT. Software development industry has started getting pressure from regulators to consider green software development. As a result, green attributes of software products are gaining importance as quality attributes. In this study, we evaluate environmental sustainability and...

Bayesian Networks (BN) have been used for decision making in software engineering for many years. We investigate the current status of BNs in predicting software quality in three aspects: 1) techniques used for parameter learning, 2) techniques used for structure learning, and 3) type of variables that represent BN nodes. We performed a systematic...

Forming teams from a large groups of developers and testers pose an important problem for software project management. There are only a few empirical studies on the topic of team evolution and the factors affecting it in software projects. In this paper, we analyzed the evolution of globally distributed testing and coding teams developing large ent...

Recommendation systems in software engineering (SE) should be designed to integrate evidence into practitioners experience. Bayesian networks (BNs) provide a natural statistical framework for evidence-based decision-making by incorporating an integrated summary of the available evidence and associated uncertainty (of consequences). In this study, w...

Defect prediction is a well-established research area in software engineering . Prediction models in the literature do not predict defect-prone modules in different test phases. We investigate the relationships between defects and test phases in order to build defect prediction models for different test phases. We mined the version history of a lar...

This article provides an empirical examination of the Turkish software industry to understand how its current profile, managerial practices and perceptions are shaped by the context of an emerging economy and its cultural background. In comparison to a developed economy with a very different culture, Austria, employees, know-how, and access to new...

Background: Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred conside...

To learn more about how software developers can integrate green software practices, the guest editors spoke with Steve Raspudic, manager and deployment and provisioning architect at IBM Toronto's Software Lab.

Most studies and regulatory controls focus on hardware-related measurement, analysis, and control for energy consumption. However, all forms of hardware include significant software components. Although software systems don't consume energy directly, they affect hardware utilization, leading to indirect energy consumption. Therefore, it's important...

One way to implement and evaluate the effectiveness of recommendation systems in software engineering is to conduct field studies. Field studies are important as they are the extension of laboratory experiments into real-life situations of organizations and/or society. They bring greater realism to the phenomena that are under study. However, field...

One way to implement and evaluate the effectiveness of recommendation systems for software engineering is to conduct field studies. Field studies are important as they are the extension of the laboratory experiments into real life situations of organizations and/or society. They bring greater realism to the phenomenon that is under study. However,...

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 confirma...

Background: In our previous research, we built defect pre- diction 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 confir...

PROMISE conference is an annual forum for researchers and practitioners to present, discuss and exchange ideas, results, expertise and experiences in construction and/or application of prediction models in software engineering. Such models could be targeted at: planning, design, implementation, testing, maintenance, quality assurance, evaluation, p...

The goal of software metrics is the identification and measurement of the essential parameters that affect software development. Metrics can be used to improve software quality and productivity. Existing metrics in the literature are mostly product or process related. However, thought processes of people have a significant impact on software qualit...

Software analytics guide practitioners in decision making throughout the software development process. In this context, prediction models help managers efficiently organize their resources and identify problems by analyzing patterns on existing project data in an intelligent and meaningful manner. Over the past decade, the authors have worked with...

During software development life cycle (SDLC), source codes are created and updated by groups of one or more developers. Information about the defect rates introduced by developer groups for the current release of a software product might guide project managers to form developer groups in order to manage defect rates for the next releases. In this...

ContextDefect prediction research mostly focus on optimizing the performance of models that are constructed for isolated projects (i.e. within project (WP)) through retrospective analyses. On the other hand, recent studies try to utilize data across projects (i.e. cross project (CP)) for building defect prediction models for new projects. There are...

Predictive models that use machine learning techniques has been useful tools to guide software project managers in making decisions under uncertainty. However in practice collecting metrics or defect data has been a troublesome job and researchers often have to deal with incomplete datasets in their studies. As a result both researchers and practit...

Developer teams may naturally emerge independent of managerial decisions, organizational structure, or work locations in large software. Such self organized collaboration teams of developers can be traced from the source code repositories. In this paper, we identify the developer teams in the collaboration network in order to present the work team...

Trademarks Save energy with the DB2 10.1 for Linux, UNIX, and Windows data compression feature Page 1 of 7 Save energy with the DB2 10.1 for Linux, UNIX, and Windows data compression feature Reduce maintenance and make your database greener Andriy Miranskyy (andriy@ca.ibm.com) Software engineer IBM Sedef Akinli Kocak (sedef. The IBM® DB2® for Linux...

Abstract The most visible area where Software Engineering (SE) and Artificial Intelligence (AI) research intersect is the predictive modeling for (guiding) SE activities, usually focused around quality assurance. For many decades, research efforts have been scattered ...

http://dl.acm.org/citation.cfm?id=2593801

Thought processes and cognitive aspects of people have a significant impact on software quality, as software is designed, implemented and tested by people. In this preliminary research, we conducted a field study during a 24 hour non-stop exploratory software development event: " hackathon". During hackathons, people collaborate intensively on soft...

Issue management is one of the major challenges of software development teams. Balanced workload allocation of developers who are responsible for the maintenance of the software product would impact the long-term reliability of the product. In this paper, we analyse the issue report, issue ownership, and issue resolve patterns of two large software...

We present an integrated measurement and defect prediction tool: Dione. Our tool enables organizations to measure, monitor, and control product quality through learning based defect prediction. Similar existing tools either provide data collection and analytics, or work just as a prediction engine. Therefore, companies need to deal with multiple to...

The thought processes of people have a significant impact on software quality, as software is designed, developed and tested by people. Cognitive biases, which are defined as patterned deviations of human thought from the laws of logic and mathematics, are a likely cause of software defects. However, there is little empirical evidence to date to su...

Some research papers whose results verify research community's progress in using data mining to organize test resources to fight defects with verifiable results based on public-domain data sets are presented. 'Applying the Mahalonobis-Taguchi strategy for software defect diagnosis' uses a well known technique in other domains for the first time in...

This special issue of IEEE Software explores the challenges in developing compliant software systems. Typically, organizations face conflicting objectives, with compliance policies possibly hindering innovation, slowing down the product development process, or making the whole process most costly. The goal of software engineering for compliance is...

Background: There are too many design options for software effort estimators. How can we best explore them all? Aim: We seek aspects on general principles of effort estimation that can guide the design of effort estimators. Method: We identified the essential assumption of analogy-based effort estimation: i.e. the immediate neighbors of a project o...

This workshop introduces software architecture concepts and their incorporation into computer science and software engineering curricula. Participants will learn techniques used in industry to specify quality attributes critical to architecture and use those attributes to drive the system structure using common architectural styles. Exercises will...

The aim of the research is to explore the impact of cognitive biases and social networks in testing and developing software. The research will aim to address two critical areas: i) to predict defective parts of the software, ii) to determine the right person to test the defective parts of the software. Every phase in software development requires a...

Defect prediction research mostly focus on optimizing the performance of models that are constructed for isolated projects. On the other hand, recent studies try to utilize data across projects for building defect prediction models. We combine both approaches and investigate the effects of using mixed (i.e. within and cross) project data on defect...

In this paper, the pricing and the transmission power control processes are investigated in a cognitive radio network. In the given network, there are multiple primary service providers (PSPs) which have some amount of unutilised bandwidth, and multiple secondary users (SUs) that require spectrum bands. SUs are assumed to pay the PSPs for short-ter...

As the application layer in embedded systems dominates over the hardware, ensuring software quality becomes a real challenge. Software testing is the most time-consuming and costly project phase, specifically in the embedded software domain. Misclassifying a safe code as defective increases the cost of projects, and hence leads to low margins. In t...

Software cost/effort estimation is still an open challenge. Many researchers have proposed various methods that usually focus on point estimates. Until today, software cost estimation has been treated as a regression problem. However, in order to prevent overestimates and underestimates, it is more practical to predict the interval of estimations i...

Software effort estimation is critical for resource allocation and planning. Accurate estimates enable managers to distribute the workload among resources in a balanced manner. The actual workload of developers may be different from the values observed in project management tools. In this research, we provide a summary of our experiences regarding:...

Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model...

Defect prediction has been evolved with variety of metric sets, and defect types. Researchers found code, churn, and network metrics as significant indicators of defects. However, all metric sets may not be informative for all defect categories such that only one metric type may represent majority of a defect category. Our previous study showed tha...

People are the most important pillar of software development process. It is critical to understand how they interact with each other and how these interactions affect the quality of the end product in terms of defects. In this research we propose to include a new set of metrics, a.k.a. social network metrics on issue repositories in predicting defe...