Software development metrics prediction using time series methods

The software development process is an intricate task, with the growing complexity of software solutions and inflating code-line count being part of the reason for the fall of software code coherence and readability thus being one of the causes for software faults and it’s declining quality. Debugging software during development is significantly less expensive than attempting damage control after the software’s release. An automated quality-related analysis of developed code, which includes code analysis and correlation of development data like an ideal solution. In this paper the ability to predict software faults and software quality is scrutinized. Hereby we investigate four models that can be used to analyze time-based data series for prediction of trends observed in the software development process are investigated. Those models are Exponential Smoothing, the Holt-Winters Model, Autoregressive Integrated Moving Average (ARIMA) and Recurrent Neural Networks (RNN). Time-series analysis methods prove a good fit for software related data prediction. Such methods and tools can lend a helping hand for Product Owners in their daily decision-making process as related to e.g. assignment of tasks, time predictions, bugs predictions, time to release etc. Results of the research are presented.Peer ReviewedPostprint (author's final draft

PROGRESS TOWARDS LEAN THINKING THROUGH IMPLEMENTATION OF TRADITIONAL VALUE STREAM MAPPING OF MANUFACTURING PROCESS. CASE: VILPE OY.

Use of Lean philosophy has resulted in many benefits for the companies, including reduced lead time, improved quality of products, greater productivity and smoother operations. Lean consists of many tools and methods which help to minimize inefficiency for example through identification and elimination of 8 types of waste. One of Lean tools called Value Stream Mapping (VSM) was applied in case company in order to discover areas for improvement of the process within VILPE Oy. The objectives of the study were to identify what wastes can be found in the company through implementation of traditional VSM and suggest methods of reduction or better control over found waste. VSM tool can be combined with other seven mapping tools for better identification of wastes. However, as wastes in the form of transportation and motion were identified in the company before – only traditional VSM was applied in this study. In addition, future value stream map is not created, because standard improvement methods usually utilized after implementation of VSM are not used. Instead as inventories, especially end-product (finished goods) inventory, were discovered to be main waste, further analysis of inventories was performed. Analysis revealed that end-product inventory is anticipation inventory. Solutions for better control of anticipation inventory are formed based on theories of inventory management and demand forecasting. One solution suggests to use numbers forecasted through Holt-Winters model in reorder point calculation, while another solution suggests more simple way to consider seasonality in reorder point calculation. Currently reorder point is calculated based on average demand, what is not suitable for products with seasonality. Adjustments in reorder point together with improved qualitative forecasting are suggested as measures for better control over anticipation inventory.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Empirical study of the effect of stochastic variability on the performance of human-dependent flexible flow lines

Manufacturing systems have developed both physically and technologically, allowing production of innovative new products in a shorter lead time, to meet the 21st century market demand. Flexible flow lines for instance use flexible entities to generate multiple product variants using the same routing. However, the variability within the flow line is asynchronous and stochastic, causing disruptions to the throughput rate. Current autonomous variability control approaches decentralise the autonomous decision allowing quick response in a dynamic environment. However, they have limitations, e.g., uncertainty that the decision is globally optimal and applicability to limited decisions. This research presents a novel formula-based autonomous control method centered on an empirical study of the effect of stochastic variability on the performance of flexible human-dependent serial flow lines. At the process level, normal distribution was used and generic nonlinear terms were then derived to represent the asynchronous variability at the flow line level. These terms were shortlisted based on their impact on the throughput rate and used to develop the formula using data mining techniques. The developed standalone formulas for the throughput rate of synchronous and asynchronous human-dependent flow lines gave steady and accurate results, higher than closest rivals, across a wide range of test data sets. Validation with continuous data from a real-world case study gave a mean absolute percentage error of 5%. The formula-based autonomous control method quantifies the impact of changes in decision variables, e.g., routing, arrival rate, etc., on the global delivery performance target, i.e., throughput, and recommends the optimal decisions independent of the performance measures of the current state. This approach gives robust decisions using pre-identified relationships and targets a wider range of decision variables. The performance of the developed autonomous control method was successfully validated for process, routing and product decisions using a standard 3x3 flexible flow line model and the real-world case study. The method was able to consistently reach the optimal decisions that improve local and global performance targets, i.e., throughput, queues and utilisation efficiency, for static and dynamic situations. For the case of parallel processing which the formula cannot handle, a hybrid autonomous control method, integrating the formula-based and an existing autonomous control method, i.e., QLE, was developed and validated.InnovateU

Design requirements for SRB production control system. Volume 5: Appendices

A questionnaire to be used to screen potential candidate production control software packages is presented