Endüstri Mühendisliği Bölümü Tez Koleksiyonuhttps://hdl.handle.net/11655/4282024-09-15T00:57:14Z2024-09-15T00:57:14ZSelf-Starting Control Charts in Software Development ProjectsTakıl, Melikehttps://hdl.handle.net/11655/343602023-12-21T06:32:19Z2023-01-01T00:00:00ZSelf-Starting Control Charts in Software Development Projects
Takıl, Melike
To improve processes, generally some metrics are defined, data are collected and these are analyzed by appropriate methods. Statistical process control (SPC) is one of the methods used to improve processes. Maybe the most common tool of SPC is control charts, which are used to monitor changes in a process or characteristic over time. Control charts were originally developed for the mass production industry where production amounts are large. In such environments, a Phase I study is carried out with sufficient amount of data to estimate in-control process parameters and to determine control limits. Nevertheless, in some environments data is not abundant as in mass production and alternative approaches are needed to estimate process parameters and design control charts. For short production runs, where there is few data available to estimate parameters, self-starting control charts were developed.
Recently, the SPC has expanded beyond manufacturing and has started to be used for process improvement in software development processes. Just like in short production runs, there may not be enough data available to estimate parameters for designing control charts in software development processes. In this study, self-starting methods and change point models are considered for monitoring performance of software development processes over time.
Main performance metrics used in software development processes are generally counts, which have discrete probability distributions. Performance measures can be used to monitor software development processes as well as to monitor project scope. Examples of these metrics could be the number of defects calculated for different scenarios or the number of requests from customers. In this thesis, a simulation study was conducted based on the assumption that the data follows a Poisson distribution. Different project durations, change points, and shift magnitudes in the parameters were compared using self-starting methods Q and Exponentially Weighted Moving Average (EWMA) Q control charts, and a change point method Generalized Likelihood Ratio (GLR) control chart. Comparisons were made based on the probabilities of giving an out-of-control signal during the project duration, and after a change point in the case of a shift from the mean of a software quality metric.
The selected control charts were applied to monitor real software development process metrics and obtained results were compared. In addition to comparing the performance of Q, EWMA Q, and GLR control charts, robustness was evaluated by taking into account the responses to different change points.
The thesis demonstrates the applicability of self-starting and change point methods designed for Poisson distributed data in software development processes and presents comparisons of these methods under different scenarios.
2023-01-01T00:00:00ZRegresyon ve Bulanık Regresyon Yöntemleri Kullanılarak Teslim Sürelerinin TahminlenmesiUçgun, Nerminhttps://hdl.handle.net/11655/342862023-12-27T08:07:12Z2023-01-01T00:00:00ZRegresyon ve Bulanık Regresyon Yöntemleri Kullanılarak Teslim Sürelerinin Tahminlenmesi
Uçgun, Nermin
Lead time estimation plays a critical role in supply chain management by enabling timely order fulfillment and optimizing the operations of organizations. Accurate lead time estimation is crucial not only for ensuring customer satisfaction but also for important considerations in stock, time, and cost planning. Various statistical methods are used for forecasting, and one of them is regression analysis. The statistical method used to analyze the relationship between two or more variables is called regression analysis. On the other hand, fuzzy regression analysis is an alternative method used in cases where the application of classical regression is not recommended due to the fact that some or all of the data is fuzzy in system structures with uncertainty or because the system structure does not allow to define precise relationships between variables. In this study, classical linear regression and fuzzy linear regression which is one of the applications based on fuzzy logic was examined theoretically and then, an application was made on lead time estimation of productions in an R&D company serving in the defense industry and the results were examined.
2023-01-01T00:00:00ZSezgisel Bulanık Marcos Yöntemi Kullanılarak Hata Modu ve Etki AnaliziAkkuş, Dilarahttps://hdl.handle.net/11655/342432023-12-18T12:24:19Z2023-01-01T00:00:00ZSezgisel Bulanık Marcos Yöntemi Kullanılarak Hata Modu ve Etki Analizi
Akkuş, Dilara
Risk analysis plays an essential role for both production and service sectors in terms of safety. Among many risk analysis methods, one of the most well-known method is Failure Mode and Effect Analysis (FMEA). The purpose of FMEA is to prevent existing Failure Modes (FMs) as well as to eliminate possible FMs that are not in the current situation at the source, and to prevent the effects that will arise in the event of these FMs. In this method, data is obtained by taking evaluations from field experts, and subjectivity arising from the experiences, value judgement and personal opinions of them directly affects the results. Therefore, when it is being utilized, usage of linguistic expressions instead of crisp numbers accelerates the data collection phase in practice and reduces the error. Due to the fact that FMEA method contains incomplete, doubtful, approximate and imprecise data sets, it is recommended to implement intuitionistic fuzzy set approach to the traditional FMEA method. This is primarily because intuitionistic fuzzy systems are pretty beneficial in problems that inhere ambiguity and hesitation. In this study, while prioritizing risks a relatively new method, Measurement Alternatives and Ranking according to the COmpromise Solution (MARCOS) method, will be used and it will be used for the first time for risk analysis under Intuitionistic Fuzzy (IF) environment. In addition, intuitionistic fuzzy weights will be assigned to each expert based on their evaluations of each other, and these weights will be taken into account when evaluating Risk Factors (RFs) and FMs.
2023-01-01T00:00:00ZPredicting Solar Energy Production Using Incremental Machine Learning TechniquesKapusızoğlu, Semanurhttps://hdl.handle.net/11655/342392023-12-12T11:19:35Z2023-01-01T00:00:00ZPredicting Solar Energy Production Using Incremental Machine Learning Techniques
Kapusızoğlu, Semanur
Energy is a significant part of life and the economy, with an aggressively increasing demand due to population growth. Non-renewable sources, such as fossil fuels, are rapidly depleting and cannot meet the demand, leading to a reliance on different energy sources. Considering the environmental effects of fossil fuels, many individuals are leaning towards cleaner and renewable energy sources, such as solar and wind power. Solar power holds an important share due to its abundance and ease of implementation. The amount of solar energy produced depends on various factors, such as temperature, photovoltaic radiation, cloud cover, and location. Predictive models considering those factors for solar energy play a crucial role in creating efficient production and distribution networks. Machine learning models are becoming increasingly popular among other predictive approaches thanks to technological advancements. Machine learning is an area of programming that creates mathematical algorithms and models, enabling computers to learn and make predictions without explicit programming. There are different training approaches for machine learning models. The traditional approach divides data into training and testing sets and uses all training data at once. Online (Incremental) learning is the principle of feeding the prediction model with one data point from a training set at a time, often used in sectors where data patterns are variable. This principle can be adapted to various data mining algorithms, including supervised and unsupervised learning. In this study, the suitability of the incremental training approach is tested on solar energy production using six different machine learning models (Linear Regression, Lasso Regression, Ridge Regression, Decision Tree, Random Forest, and Artificial Neural Network). An open-source competition on the Kaggle platform, provided by the American Meteorological Society, is utilized to assess whether online models can outperform traditional models in solar energy predictions. Incremental training methods found to perform better than traditional methods in terms of Mean Absolute Error.
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