第2章計算論的思考と設計の再利用性

Computers can be used to help us solve problems. However, before a problem can be tackled, the problem itself and how it could be solved need to be understood. Computational thinking transcends mere programming; it doesn’t equate to thinking in the binary fashion of computers, as they fundamentally lack the capacity for thought. Rather, while programming is the craft of instructing a computer on the actions to execute, computational thinking empowers you to meticulously determine what those instructions should be. Take, for instance, the strategic gameplay involved in chess. To excel in a chess match, a player must:

• Understand the unique movements and strategic values of each piece, recognizing how each can be maneuvered to control the board.

• Visualize the board’s layout, identifying potential threats and opportunities, and planning moves several steps ahead to secure an advantageous position.

• Recognize patterns from previous games, understanding common tactics and counters, to formulate a robust, adaptable strategy.

In devising a winning strategy, computational thinking is the underpinning framework:

• The complex game is dissected into smaller, more manageable components (e.g., the function of each chess piece, the state of the board)—this is decomposition.

• Attention is concentrated on pivotal elements that influence the game’s outcome, such as the positioning of key pieces and the opponent’s tendencies, sidelining less critical factors—this is an abstraction.

• Drawing from prior knowledge and experiences in similar scenarios, a step-by-step approach is developed to navigate through the game—this is algorithmic thinking.

Should you venture into developing your own chess program or strategy, these are precisely the types of considerations you would deliberate on and resolve before actual programming.

Testing and debugging are techniques used to identify flaws in algorithms and defects in code to be able to correct them. Test cases rely on providing specific input data to check whether a software program functions correctly and meets its designed requirements. Test cases need to be identified to drive tests. If a test associated with a test case fails, debugging needs to be conducted to identify the source of the problem and correct it. In other words, debugging is about locating and fixing defects (i.e., bugs) in algorithms and processes to make them behave as expected. In programming, everyone makes mistakes, they are part of the learning process. The important thing is to identify the mistake and work out how to overcome it. There are those who feel that the deepest learning takes place when mistakes are made.

In the jam sandwich algorithm, testing can be facilitated by taking turns to play the role of the programmer who gives instructions as well as the robot. If you are a programmer, your job is to read out the instructions and follow each step. You can choose to follow your pseudocode or your flowchart. Each instruction becomes a test case, and the test succeeds if the robot can follow every instruction exactly and successfully. In the alternative, you will need to debug the instruction to identify the source of the problem and correct it. Table 2.2 can be used to record problems encountered and improvements that need to be made.

Computational thinking is important in every industry today. In DNA sequencing, computational thinking helps manage the massive and complex data involved. It starts by breaking the large DNA sequence into smaller pieces. Then, it involves identifying patterns or sequences within these pieces, which might indicate genetic information like the presence of certain genes. The focus is on the most relevant parts of the sequence, discarding unnecessary data to concentrate on potentially significant genetic regions. Finally, refined algorithms process and reconstruct the original sequence to identify genetic variations. This approach is used for efficiently handling massive datasets in DNA sequencing and extracting meaningful insights. The parts of computational thinking (CT) can be identified and highlighted in the process of DNA sequencing, a complex task within the field of genomics:

• Decomposition: Break down the DNA sequencing process into specific steps such as sample collection and DNA extraction.

• Pattern recognition: Identify similarities in DNA sequences that could indicate genetic traits or anomalies.

• Abstraction: Focus on the essential parts of the genetic information that are relevant for the study at hand.

• Algorithms: Create step-by-step protocols for each part of the sequencing process.

• Logical thinking: Determine the most accurate methods for sequencing based on the type of sample and the required depth of sequence analysis.

• Evaluation: Assess the quality and accuracy of the sequencing data obtained.

• Debugging: Identify issues that may arise during the sequencing process.